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   "source": [
    "# Generative Adversarial Networks (GANs)\n",
    "\n",
    "So far in CS231N, all the applications of neural networks that we have explored have been **discriminative models** that take an input and are trained to produce a labeled output. This has ranged from straightforward classification of image categories to sentence generation (which was still phrased as a classification problem, our labels were in vocabulary space and we’d learned a recurrence to capture multi-word labels). In this notebook, we will expand our repetoire, and build **generative models** using neural networks. Specifically, we will learn how to build models which generate novel images that resemble a set of training images.\n",
    "\n",
    "### What is a GAN?\n",
    "\n",
    "In 2014, [Goodfellow et al.](https://arxiv.org/abs/1406.2661) presented a method for training generative models called Generative Adversarial Networks (GANs for short). In a GAN, we build two different neural networks. Our first network is a traditional classification network, called the **discriminator**. We will train the discriminator to take images, and classify them as being real (belonging to the training set) or fake (not present in the training set). Our other network, called the **generator**, will take random noise as input and transform it using a neural network to produce images. The goal of the generator is to fool the discriminator into thinking the images it produced are real.\n",
    "\n",
    "We can think of this back and forth process of the generator ($G$) trying to fool the discriminator ($D$), and the discriminator trying to correctly classify real vs. fake as a minimax game:\n",
    "$$\\underset{G}{\\text{minimize}}\\; \\underset{D}{\\text{maximize}}\\; \\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] + \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n",
    "where $z \\sim p(z)$ are the random noise samples, $G(z)$ are the generated images using the neural network generator $G$, and $D$ is the output of the discriminator, specifying the probability of an input being real. In [Goodfellow et al.](https://arxiv.org/abs/1406.2661), they analyze this minimax game and show how it relates to minimizing the Jensen-Shannon divergence between the training data distribution and the generated samples from $G$.\n",
    "\n",
    "To optimize this minimax game, we will aternate between taking gradient *descent* steps on the objective for $G$, and gradient *ascent* steps on the objective for $D$:\n",
    "1. update the **generator** ($G$) to minimize the probability of the __discriminator making the correct choice__. \n",
    "2. update the **discriminator** ($D$) to maximize the probability of the __discriminator making the correct choice__.\n",
    "\n",
    "While these updates are useful for analysis, they do not perform well in practice. Instead, we will use a different objective when we update the generator: maximize the probability of the **discriminator making the incorrect choice**. This small change helps to allevaiate problems with the generator gradient vanishing when the discriminator is confident. This is the standard update used in most GAN papers, and was used in the original paper from [Goodfellow et al.](https://arxiv.org/abs/1406.2661). \n",
    "\n",
    "In this assignment, we will alternate the following updates:\n",
    "1. Update the generator ($G$) to maximize the probability of the discriminator making the incorrect choice on generated data:\n",
    "$$\\underset{G}{\\text{maximize}}\\;  \\mathbb{E}_{z \\sim p(z)}\\left[\\log D(G(z))\\right]$$\n",
    "2. Update the discriminator ($D$), to maximize the probability of the discriminator making the correct choice on real and generated data:\n",
    "$$\\underset{D}{\\text{maximize}}\\; \\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] + \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n",
    "\n",
    "### What else is there?\n",
    "Since 2014, GANs have exploded into a huge research area, with massive [workshops](https://sites.google.com/site/nips2016adversarial/), and [hundreds of new papers](https://github.com/hindupuravinash/the-gan-zoo). Compared to other approaches for generative models, they often produce the highest quality samples but are some of the most difficult and finicky models to train (see [this github repo](https://github.com/soumith/ganhacks) that contains a set of 17 hacks that are useful for getting models working). Improving the stabiilty and robustness of GAN training is an open research question, with new papers coming out every day! For a more recent tutorial on GANs, see [here](https://arxiv.org/abs/1701.00160). There is also some even more recent exciting work that changes the objective function to Wasserstein distance and yields much more stable results across model architectures: [WGAN](https://arxiv.org/abs/1701.07875), [WGAN-GP](https://arxiv.org/abs/1704.00028).\n",
    "\n",
    "\n",
    "GANs are not the only way to train a generative model! For other approaches to generative modeling check out the [deep generative model chapter](http://www.deeplearningbook.org/contents/generative_models.html) of the Deep Learning [book](http://www.deeplearningbook.org). Another popular way of training neural networks as generative models is Variational Autoencoders (co-discovered [here](https://arxiv.org/abs/1312.6114) and [here](https://arxiv.org/abs/1401.4082)). Variatonal autoencoders combine neural networks with variationl inference to train deep generative models. These models tend to be far more stable and easier to train but currently don't produce samples that are as pretty as GANs.\n",
    "\n",
    "Here's an example of what your outputs from the 3 different models you're going to train should look like... note that GANs are sometimes finicky, so your outputs might not look exactly like this... this is just meant to be a *rough* guideline of the kind of quality you can expect:\n",
    "\n",
    "![caption](gan_outputs_pytorch.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setup"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "import torch.nn as nn\n",
    "from torch.nn import init\n",
    "import torchvision\n",
    "import torchvision.transforms as T\n",
    "import torch.optim as optim\n",
    "from torch.utils.data import DataLoader\n",
    "from torch.utils.data import sampler\n",
    "import torchvision.datasets as dset\n",
    "\n",
    "import numpy as np\n",
    "\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.gridspec as gridspec\n",
    "\n",
    "%matplotlib inline\n",
    "plt.rcParams['figure.figsize'] = (10.0, 8.0) # set default size of plots\n",
    "plt.rcParams['image.interpolation'] = 'nearest'\n",
    "plt.rcParams['image.cmap'] = 'gray'\n",
    "\n",
    "def show_images(images):\n",
    "    images = np.reshape(images, [images.shape[0], -1])  # images reshape to (batch_size, D)\n",
    "    sqrtn = int(np.ceil(np.sqrt(images.shape[0])))\n",
    "    sqrtimg = int(np.ceil(np.sqrt(images.shape[1])))\n",
    "\n",
    "    fig = plt.figure(figsize=(sqrtn, sqrtn))\n",
    "    gs = gridspec.GridSpec(sqrtn, sqrtn)\n",
    "    gs.update(wspace=0.05, hspace=0.05)\n",
    "\n",
    "    for i, img in enumerate(images):\n",
    "        ax = plt.subplot(gs[i])\n",
    "        plt.axis('off')\n",
    "        ax.set_xticklabels([])\n",
    "        ax.set_yticklabels([])\n",
    "        ax.set_aspect('equal')\n",
    "        plt.imshow(img.reshape([sqrtimg,sqrtimg]))\n",
    "    return \n",
    "\n",
    "def preprocess_img(x):\n",
    "    return 2 * x - 1.0\n",
    "\n",
    "def deprocess_img(x):\n",
    "    return (x + 1.0) / 2.0\n",
    "\n",
    "def rel_error(x,y):\n",
    "    return np.max(np.abs(x - y) / (np.maximum(1e-8, np.abs(x) + np.abs(y))))\n",
    "\n",
    "def count_params(model):\n",
    "    \"\"\"Count the number of parameters in the current TensorFlow graph \"\"\"\n",
    "    param_count = np.sum([np.prod(p.size()) for p in model.parameters()])\n",
    "    return param_count\n",
    "\n",
    "answers = dict(np.load('gan-checks-tf.npz'))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Dataset\n",
    " GANs are notoriously finicky with hyperparameters, and also require many training epochs. In order to make this assignment approachable without a GPU, we will be working on the MNIST dataset, which is 60,000 training and 10,000 test images. Each picture contains a centered image of white digit on black background (0 through 9). This was one of the first datasets used to train convolutional neural networks and it is fairly easy -- a standard CNN model can easily exceed 99% accuracy. \n",
    "\n",
    "To simplify our code here, we will use the PyTorch MNIST wrapper, which downloads and loads the MNIST dataset. See the [documentation](https://github.com/pytorch/vision/blob/master/torchvision/datasets/mnist.py) for more information about the interface. The default parameters will take 5,000 of the training examples and place them into a validation dataset. The data will be saved into a folder called `MNIST_data`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9de0926eb8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "class ChunkSampler(sampler.Sampler):\n",
    "    \"\"\"Samples elements sequentially from some offset. \n",
    "    Arguments:\n",
    "        num_samples: # of desired datapoints\n",
    "        start: offset where we should start selecting from\n",
    "    \"\"\"\n",
    "    def __init__(self, num_samples, start=0):\n",
    "        self.num_samples = num_samples\n",
    "        self.start = start\n",
    "\n",
    "    def __iter__(self):\n",
    "        return iter(range(self.start, self.start + self.num_samples))\n",
    "\n",
    "    def __len__(self):\n",
    "        return self.num_samples\n",
    "\n",
    "NUM_TRAIN = 50000\n",
    "NUM_VAL = 5000\n",
    "\n",
    "NOISE_DIM = 96\n",
    "batch_size = 128\n",
    "\n",
    "mnist_train = dset.MNIST('./cs231n/datasets/MNIST_data', train=True, download=True,\n",
    "                           transform=T.ToTensor())\n",
    "loader_train = DataLoader(mnist_train, batch_size=batch_size,\n",
    "                          sampler=ChunkSampler(NUM_TRAIN, 0))\n",
    "\n",
    "mnist_val = dset.MNIST('./cs231n/datasets/MNIST_data', train=True, download=True,\n",
    "                           transform=T.ToTensor())\n",
    "loader_val = DataLoader(mnist_val, batch_size=batch_size,\n",
    "                        sampler=ChunkSampler(NUM_VAL, NUM_TRAIN))\n",
    "\n",
    "\n",
    "imgs = loader_train.__iter__().next()[0].view(batch_size, 784).numpy().squeeze()\n",
    "show_images(imgs)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Random Noise\n",
    "Generate uniform noise from -1 to 1 with shape `[batch_size, dim]`.\n",
    "\n",
    "Hint: use `torch.rand`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "def sample_noise(batch_size, dim):\n",
    "    \"\"\"\n",
    "    Generate a PyTorch Tensor of uniform random noise.\n",
    "\n",
    "    Input:\n",
    "    - batch_size: Integer giving the batch size of noise to generate.\n",
    "    - dim: Integer giving the dimension of noise to generate.\n",
    "    \n",
    "    Output:\n",
    "    - A PyTorch Tensor of shape (batch_size, dim) containing uniform\n",
    "      random noise in the range (-1, 1).\n",
    "    \"\"\"\n",
    "    return torch.rand((batch_size, dim))*2-1\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Make sure noise is the correct shape and type:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "All tests passed!\n"
     ]
    }
   ],
   "source": [
    "def test_sample_noise():\n",
    "    batch_size = 3\n",
    "    dim = 4\n",
    "    torch.manual_seed(231)\n",
    "    z = sample_noise(batch_size, dim)\n",
    "    np_z = z.cpu().numpy()\n",
    "    assert np_z.shape == (batch_size, dim)\n",
    "    assert torch.is_tensor(z)\n",
    "    assert np.all(np_z >= -1.0) and np.all(np_z <= 1.0)\n",
    "    assert np.any(np_z < 0.0) and np.any(np_z > 0.0)\n",
    "    print('All tests passed!')\n",
    "    \n",
    "test_sample_noise()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Flatten\n",
    "\n",
    "Recall our Flatten operation from previous notebooks... this time we also provide an Unflatten, which you might want to use when implementing the convolutional generator. We also provide a weight initializer (and call it for you) that uses Xavier initialization instead of PyTorch's uniform default."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "class Flatten(nn.Module):\n",
    "    def forward(self, x):\n",
    "        N, C, H, W = x.size() # read in N, C, H, W\n",
    "        return x.view(N, -1)  # \"flatten\" the C * H * W values into a single vector per image\n",
    "    \n",
    "class Unflatten(nn.Module):\n",
    "    \"\"\"\n",
    "    An Unflatten module receives an input of shape (N, C*H*W) and reshapes it\n",
    "    to produce an output of shape (N, C, H, W).\n",
    "    \"\"\"\n",
    "    def __init__(self, N=-1, C=128, H=7, W=7):\n",
    "        super(Unflatten, self).__init__()\n",
    "        self.N = N\n",
    "        self.C = C\n",
    "        self.H = H\n",
    "        self.W = W\n",
    "    def forward(self, x):\n",
    "        return x.view(self.N, self.C, self.H, self.W)\n",
    "\n",
    "def initialize_weights(m):\n",
    "    if isinstance(m, nn.Linear) or isinstance(m, nn.ConvTranspose2d):\n",
    "        init.xavier_uniform_(m.weight.data)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## CPU / GPU\n",
    "By default all code will run on CPU. GPUs are not needed for this assignment, but will help you to train your models faster. If you do want to run the code on a GPU, then change the `dtype` variable in the following cell."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "dtype = torch.FloatTensor\n",
    "#dtype = torch.cuda.FloatTensor ## UNCOMMENT THIS LINE IF YOU'RE ON A GPU!"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Discriminator\n",
    "Our first step is to build a discriminator. Fill in the architecture as part of the `nn.Sequential` constructor in the function below. All fully connected layers should include bias terms. The architecture is:\n",
    " * Fully connected layer with input size 784 and output size 256\n",
    " * LeakyReLU with alpha 0.01\n",
    " * Fully connected layer with input_size 256 and output size 256\n",
    " * LeakyReLU with alpha 0.01\n",
    " * Fully connected layer with input size 256 and output size 1\n",
    " \n",
    "Recall that the Leaky ReLU nonlinearity computes $f(x) = \\max(\\alpha x, x)$ for some fixed constant $\\alpha$; for the LeakyReLU nonlinearities in the architecture above we set $\\alpha=0.01$.\n",
    " \n",
    "The output of the discriminator should have shape `[batch_size, 1]`, and contain real numbers corresponding to the scores that each of the `batch_size` inputs is a real image."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator():\n",
    "    \"\"\"\n",
    "    Build and return a PyTorch model implementing the architecture above.\n",
    "    \"\"\"\n",
    "    model = nn.Sequential(\n",
    "        Flatten(),\n",
    "        nn.Linear(784, 256),\n",
    "        nn.LeakyReLU(negative_slope=0.01, inplace=True),\n",
    "        nn.Linear(256, 256),\n",
    "        nn.LeakyReLU(negative_slope=0.01, inplace=True),\n",
    "        nn.Linear(256, 1)\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test to make sure the number of parameters in the discriminator is correct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct number of parameters in discriminator.\n"
     ]
    }
   ],
   "source": [
    "def test_discriminator(true_count=267009):\n",
    "    model = discriminator()\n",
    "    cur_count = count_params(model)\n",
    "    if cur_count != true_count:\n",
    "        print('Incorrect number of parameters in discriminator. Check your achitecture.')\n",
    "    else:\n",
    "        print('Correct number of parameters in discriminator.')     \n",
    "\n",
    "test_discriminator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Generator\n",
    "Now to build the generator network:\n",
    " * Fully connected layer from noise_dim to 1024\n",
    " * `ReLU`\n",
    " * Fully connected layer with size 1024 \n",
    " * `ReLU`\n",
    " * Fully connected layer with size 784\n",
    " * `TanH` (to clip the image to be in the range of [-1,1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "def generator(noise_dim=NOISE_DIM):\n",
    "    \"\"\"\n",
    "    Build and return a PyTorch model implementing the architecture above.\n",
    "    \"\"\"\n",
    "    model = nn.Sequential(\n",
    "        nn.Linear(noise_dim, 1024),\n",
    "        nn.ReLU(inplace=True),\n",
    "        nn.Linear(1024, 1024),\n",
    "        nn.ReLU(inplace=True),\n",
    "        nn.Linear(1024, 784),\n",
    "        nn.Tanh()\n",
    "    )\n",
    "    return model"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test to make sure the number of parameters in the generator is correct:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct number of parameters in generator.\n"
     ]
    }
   ],
   "source": [
    "def test_generator(true_count=1858320):\n",
    "    model = generator(4)\n",
    "    cur_count = count_params(model)\n",
    "    if cur_count != true_count:\n",
    "        print('Incorrect number of parameters in generator. Check your achitecture.')\n",
    "    else:\n",
    "        print('Correct number of parameters in generator.')\n",
    "\n",
    "test_generator()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# GAN Loss\n",
    "\n",
    "Compute the generator and discriminator loss. The generator loss is:\n",
    "$$\\ell_G  =  -\\mathbb{E}_{z \\sim p(z)}\\left[\\log D(G(z))\\right]$$\n",
    "and the discriminator loss is:\n",
    "$$ \\ell_D = -\\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\log D(x)\\right] - \\mathbb{E}_{z \\sim p(z)}\\left[\\log \\left(1-D(G(z))\\right)\\right]$$\n",
    "Note that these are negated from the equations presented earlier as we will be *minimizing* these losses.\n",
    "\n",
    "**HINTS**: You should use the `bce_loss` function defined below to compute the binary cross entropy loss which is needed to compute the log probability of the true label given the logits output from the discriminator. Given a score $s\\in\\mathbb{R}$ and a label $y\\in\\{0, 1\\}$, the binary cross entropy loss is\n",
    "\n",
    "$$ bce(s, y) = -y * \\log(s) - (1 - y) * \\log(1 - s) $$\n",
    "\n",
    "A naive implementation of this formula can be numerically unstable, so we have provided a numerically stable implementation for you below.\n",
    "\n",
    "You will also need to compute labels corresponding to real or fake and use the logit arguments to determine their size. Make sure you cast these labels to the correct data type using the global `dtype` variable, for example:\n",
    "\n",
    "\n",
    "`true_labels = torch.ones(size).type(dtype)`\n",
    "\n",
    "Instead of computing the expectation of $\\log D(G(z))$, $\\log D(x)$ and $\\log \\left(1-D(G(z))\\right)$, we will be averaging over elements of the minibatch, so make sure to combine the loss by averaging instead of summing."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "def bce_loss(input, target):\n",
    "    \"\"\"\n",
    "    Numerically stable version of the binary cross-entropy loss function.\n",
    "\n",
    "    As per https://github.com/pytorch/pytorch/issues/751\n",
    "    See the TensorFlow docs for a derivation of this formula:\n",
    "    https://www.tensorflow.org/api_docs/python/tf/nn/sigmoid_cross_entropy_with_logits\n",
    "\n",
    "    Inputs:\n",
    "    - input: PyTorch Tensor of shape (N, ) giving scores.\n",
    "    - target: PyTorch Tensor of shape (N,) containing 0 and 1 giving targets.\n",
    "\n",
    "    Returns:\n",
    "    - A PyTorch Tensor containing the mean BCE loss over the minibatch of input data.\n",
    "    \"\"\"\n",
    "    neg_abs = - input.abs()\n",
    "    loss = input.clamp(min=0) - input * target + (1 + neg_abs.exp()).log()\n",
    "    return loss.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "def discriminator_loss(logits_real, logits_fake):\n",
    "    \"\"\"\n",
    "    Computes the discriminator loss described above.\n",
    "    \n",
    "    Inputs:\n",
    "    - logits_real: PyTorch Tensor of shape (N,) giving scores for the real data.\n",
    "    - logits_fake: PyTorch Tensor of shape (N,) giving scores for the fake data.\n",
    "    \n",
    "    Returns:\n",
    "    - loss: PyTorch Tensor containing (scalar) the loss for the discriminator.\n",
    "    \"\"\"\n",
    "    N = logits_real.shape\n",
    "    true_labels = torch.ones(N).type(dtype)\n",
    "    loss_real = bce_loss(logits_real, true_labels)\n",
    "    loss_fake = bce_loss(logits_fake, 1-true_labels)\n",
    "    loss = loss_real + loss_fake\n",
    "    return loss\n",
    "\n",
    "def generator_loss(logits_fake):\n",
    "    \"\"\"\n",
    "    Computes the generator loss described above.\n",
    "\n",
    "    Inputs:\n",
    "    - logits_fake: PyTorch Tensor of shape (N,) giving scores for the fake data.\n",
    "    \n",
    "    Returns:\n",
    "    - loss: PyTorch Tensor containing the (scalar) loss for the generator.\n",
    "    \"\"\"\n",
    "    N = logits_fake.shape\n",
    "    true_labels = torch.ones(N).type(dtype)\n",
    "    loss = bce_loss(logits_fake, true_labels)\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Test your generator and discriminator loss. You should see errors < 1e-7."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum error in d_loss: 3.97058e-09\n"
     ]
    }
   ],
   "source": [
    "def test_discriminator_loss(logits_real, logits_fake, d_loss_true):\n",
    "    d_loss = discriminator_loss(torch.Tensor(logits_real).type(dtype),\n",
    "                                torch.Tensor(logits_fake).type(dtype)).cpu().numpy()\n",
    "    print(\"Maximum error in d_loss: %g\"%rel_error(d_loss_true, d_loss))\n",
    "\n",
    "test_discriminator_loss(answers['logits_real'], answers['logits_fake'],\n",
    "                        answers['d_loss_true'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum error in g_loss: 4.4518e-09\n"
     ]
    }
   ],
   "source": [
    "def test_generator_loss(logits_fake, g_loss_true):\n",
    "    g_loss = generator_loss(torch.Tensor(logits_fake).type(dtype)).cpu().numpy()\n",
    "    print(\"Maximum error in g_loss: %g\"%rel_error(g_loss_true, g_loss))\n",
    "\n",
    "test_generator_loss(answers['logits_fake'], answers['g_loss_true'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Optimizing our loss\n",
    "Make a function that returns an `optim.Adam` optimizer for the given model with a 1e-3 learning rate, beta1=0.5, beta2=0.999. You'll use this to construct optimizers for the generators and discriminators for the rest of the notebook."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "def get_optimizer(model):\n",
    "    \"\"\"\n",
    "    Construct and return an Adam optimizer for the model with learning rate 1e-3,\n",
    "    beta1=0.5, and beta2=0.999.\n",
    "    \n",
    "    Input:\n",
    "    - model: A PyTorch model that we want to optimize.\n",
    "    \n",
    "    Returns:\n",
    "    - An Adam optimizer for the model with the desired hyperparameters.\n",
    "    \"\"\"\n",
    "    optimizer = optim.Adam(model.parameters(), lr=1e-3,  betas=(0.5, 0.999))\n",
    "    return optimizer"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Training a GAN!\n",
    "\n",
    "We provide you the main training loop... you won't need to change this function, but we encourage you to read through and understand it. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "def run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss, show_every=250, \n",
    "              batch_size=128, noise_size=96, num_epochs=10):\n",
    "    \"\"\"\n",
    "    Train a GAN!\n",
    "    \n",
    "    Inputs:\n",
    "    - D, G: PyTorch models for the discriminator and generator\n",
    "    - D_solver, G_solver: torch.optim Optimizers to use for training the\n",
    "      discriminator and generator.\n",
    "    - discriminator_loss, generator_loss: Functions to use for computing the generator and\n",
    "      discriminator loss, respectively.\n",
    "    - show_every: Show samples after every show_every iterations.\n",
    "    - batch_size: Batch size to use for training.\n",
    "    - noise_size: Dimension of the noise to use as input to the generator.\n",
    "    - num_epochs: Number of epochs over the training dataset to use for training.\n",
    "    \"\"\"\n",
    "    iter_count = 0\n",
    "    for epoch in range(num_epochs):\n",
    "        for x, _ in loader_train:\n",
    "            if len(x) != batch_size:\n",
    "                continue\n",
    "            D_solver.zero_grad()\n",
    "            real_data = x.type(dtype)\n",
    "            logits_real = D(2* (real_data - 0.5)).type(dtype)\n",
    "\n",
    "            g_fake_seed = sample_noise(batch_size, noise_size).type(dtype)\n",
    "            fake_images = G(g_fake_seed).detach()\n",
    "            logits_fake = D(fake_images.view(batch_size, 1, 28, 28))\n",
    "\n",
    "            d_total_error = discriminator_loss(logits_real, logits_fake)\n",
    "            d_total_error.backward()        \n",
    "            D_solver.step()\n",
    "\n",
    "            G_solver.zero_grad()\n",
    "            g_fake_seed = sample_noise(batch_size, noise_size).type(dtype)\n",
    "            fake_images = G(g_fake_seed)\n",
    "\n",
    "            gen_logits_fake = D(fake_images.view(batch_size, 1, 28, 28))\n",
    "            g_error = generator_loss(gen_logits_fake)\n",
    "            g_error.backward()\n",
    "            G_solver.step()\n",
    "\n",
    "            if (iter_count % show_every == 0):\n",
    "                print('Iter: {}, D: {:.4}, G:{:.4}'.format(iter_count,d_total_error.item(),g_error.item()))\n",
    "                imgs_numpy = fake_images.data.cpu().numpy()\n",
    "                show_images(imgs_numpy[0:16])\n",
    "                plt.show()\n",
    "                print()\n",
    "            iter_count += 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 0, D: 1.328, G:0.7206\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5402240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 250, D: 1.196, G:0.8882\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd58103c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 500, D: 0.8116, G:1.3\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dccd4d160>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 750, D: 1.214, G:1.179\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dccca9cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1000, D: 1.214, G:1.002\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd548cef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1250, D: 1.238, G:0.959\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5b493c8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1500, D: 1.296, G:0.8582\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5683b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1750, D: 1.217, G:0.8479\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dccfc8128>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2000, D: 1.31, G:0.8808\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd575a240>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2250, D: 1.209, G:0.8872\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd41351d0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2500, D: 1.309, G:1.034\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd7607da0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2750, D: 1.294, G:0.8505\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5ab2e48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3000, D: 1.326, G:0.8366\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5c46cf8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3250, D: 1.344, G:0.8359\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd55956d8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3500, D: 1.306, G:0.9345\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5622550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3750, D: 1.352, G:0.7263\n"
     ]
    },
    {
     "data": {
      "image/png": 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4+0xJZcv5v79eSaHWOxdccAHg1mjppZcGnES+6qqrcn5qiqjo0qVL3myt9i4O+u2334LnVs5XJVDNPffcAGUnTyWJSWbDqBNiOcBaWlpy0jl92yWbzQY7/SKLLAK4HVlOLL/lbhxkQ7/++usArLfeegB88MEHgJMGb775JtA282iNNdYAnNTTLh+65qqcJ9IOXnvtNQCWWGIJAG688UbApUuKzz77LNBSlHMuCS323XdfAFZbbTUAdtxxR6Cyxg61dIDpeXj//fcDx5A+Z2lLksx6TvQ8jBkzBoAzzzwTcK2junfvHsywiiItB5jWRY5XrdfgwYMDjUuSWPcpH80nn3wCQP/+/QE3j7zYrO0oLDRlGA1GVYUWod8DuZJDIQbZRl9//TXg7MFrrrkGcGEaeSqLSWxNfHjmmWcAZ39r19M8o6FDhwK5qYO6tj59+gBOmle6q+vYSkOVJFpyySUBeOSRRwA49thjATe/aL755gvsKfkHpLXIyy3P+5577gnAv/71L6AyG7oWkjlUvZT3N0lVSWZpMvq9pobqGVKYTuvT3NxcUptLWjKvvfbagPOsS2uQX6S1tTX4m56rzz//HHDPhbTIJ554AnD3q8hFHM3UJLNhNBhVJY0onU52aCaTCXYmSahVV10VgNtvvx1w3kpJJdmYkq433nhjZE20jq1iCb1Hc6T+8Y9/5Lxv0UUXDXZM8f333+ccs9pa2CRK+HwfhJDmIRu7EmohmbWm8mWEmW+++QCnmclmlBdYXnTZyFr78Lwx/Vt+GD8RKC2bWeshv0c4fXnEiBGA842UWv9q2kabZDaMBqOqDDBJZNk72WyWxRZbDHAZVhdffDHgYqmnnnoqAJttthngZi3Ldi6kKeh3xx13HODsEM0FlrfRJxyz1hC6sWPH5txLtVQjkTV8TD6AQYMGAfD8888DMHDgwCqvLl2kme222255f9PnIv+APMGSzJLUPXr0ANwaK/qgLMCZM2cGa7XhhhsCMG7cuGRvJAJldfljasANMZw0aVLB9yo9WRGWWmCS2TDqhKpsZl9Sb7PNNsG/lR8tb+8WW2wBwE477QQ4z7SKJJQVVSwO59unSeTotmeSvvqQKWYpqSSP/wMPPFD1OZK0mfV56+euu+4KuLz00Dm54447ALfuvi2s/ycxHCHtNSz0nCkCoQw3fRZ6RuW1v/vuu4HK8iqE2cyG0WAkOp4mk8mw/PLLA84eVKaPbKaXXnoJyB+yLkldSYZMMaJ2/o7Q2VHXIP/BzTffDDjvdRKfRRrebHmVFTueY4458l6j3PR77rkHcBqaL5n1vLzyyiuA8xgr1l7OON+0vdnF4uj6m7IO5TMaNWoUANddd13V12GS2TAajEQkc1j6ScLKe/30008D8PbbbwNOAvfq1QtwdqFixFOnTg08gb6doawyxSRFVJPAchoMdoTCdmW/6XNMcpRtuZI5TrxcNv1tt90GuDXVZ33rrbcGayefiDQjSWjVlata6vDDDwdcDFkZVNOnTw9i0f76FrrHJNdQuRKvvvpq5GuUg614upBNnUQ1Wip9s0t9UAsssEAQRFdxgNz7SlV86KGHdKyc3ys97oYbbmDBBRcEXHjr6quvBlxXijvvvBNw7n89IPp/iXsAkiu0KJfwF1TlmOp/JZQMo2L8JEhCzfZNIDWBUMcXoQe3U6dOgTmlTV3pjHLySa2WiSGHmdZQqrsco8Vojw15zTXXBFwIzSfNDTkKU7MNo06oSjJLIobVYalV119/PeAcXXKE/POf/wTgsssuA1zSiBwhW265JWeccUbOa4cPHw64ZHY5SeRcGD16dM65wr2xfQdYrSWzbwLMmDEjSNNUSqOkz7zzzgskO10iCclczAEUhZJCdI9CiSb+Gqq9ULGOr9IQfA2s1pL5wAMPDApEfFTyqzTlJDDJbBgNRizJ3KVLlyzkG/U6xuGHHx60+Fl//fUB18hu/PjxABxwwAE57/F3+27dugW78wknnADADjvsALiQiJgwYQLgSgX9Iv9i1Co0pSSZyy+/HGjTEPzrlCRWMwKF9XSNSmM9+eSTY5+/GgeYNC9dh9Jm9RqFG4XW7YcffggSYnRcpUSqiYO0LHXljEoaCRdalHOPtfB7ZDKZyOuNspWrSXAyyWwYDUai3uzZZ589r/maGp2p6EE7mh92Ctvf2sUkwZRAoCaA2qnVaneVVVYBYKONNsq7plIhl7R2ddmK6u2tn2rFC67IwPfYJtlKNgmbWbb8hx9+CLi1kobm/3/o0KHB+ipV9bnnngNcQoyiHPJmK+miEmptM4eLP4TWO6ropxpMMhtGg5FIE3wxyyyzBLuzPI7acbVTK1lABd/a9SW5w4kekuKaQ6RSQXm+jzzySMDFPQt516MkstoZpcU+++wDOA+7Ei3efvttttpqK8AlFqj0UQk2/q5fzJsctzlCHKmvz1NljI8++igAq6++OuBSdbXWavXz008/BdEKSWIdY5dddgFc04pqJHKt0b3MmDEjiIPrc5Tf5rTTTmufi8Mks2HUDRXZzFG7e0tLSyAVfSnpJ61rHpTK5/T7FVdcMXiPbGYl9O+1116Ai0mqKF6eU2Uoha/Ll1yShqHXJmpvSVqpoZ+uXb9/6qmngnOr3ZHasUalr+raC2W4lZK0cW3msGYkSaRzfPrpp4BbW7Vt0usnT54MtK2T30hAnm81XdT6J9EsvlY2s/9chkky48vHbGbDaDCq8mYXste0Q0kCRmUzKUvLj2FuuOGGQUxSNrMklyTVvffeC7gsMiXp6150XU1NTXnSzh/HktSurvtQ7rla/my33XY517j66qsHn5f8Bv41djRvtlDxw4svvgg4f4dPa2trIL396IZaHW+99daAy1OvhrSb4Ps+nDBpSmRhktkwGoxY3myfcKmhjySyv7sJZYKpukqN3vv16xfYUarKkadbtrEqbpQpFdzM79JAEnzGjBl51yaJrDzfpFGVkDQANRwQX375JQsssADgNAi9Nk71V3ugeL480hrgJ01qyy23BNri0ar80lrIay3bOQmJnDZ6dqZMmZL3N61VR8Iks2HUCbFs5qamppwXV2LT+WNd/FrZTp06BXaWXivbUt5TFY0rq2jw4MEAbLzxxgAcdNBBQK4tr/i2rlnSr1J7y/fOK09cmoY/oE5D5YcPHx40atD9lWpml2RebxptdXxvdlNTU1CDLv+GGkpoTZIcV5uUzSwNSbXYl156KQCHHHII4HIGevfuHTQlqAVmMxtGgxFLMnfu3DkLbicu5IXV39RqVGNpfGT/aiSm6kAhvwZYdpbGsqpWVjum8mLVHD8c6/bj3r5kqHZXV7sjVQOp6fmhhx4KwP777w84Sf3EE08EXmBpGopFp0EtR7qKbt26BZqP7zOJav1TjDix9EruT9Vq8uOoPbD/zOi5fPrpp1l55ZXjnqZiUmkb1KlTpyxEh1K6detWcpZuNciZIrVaX+6//e1vOT/LUUv1kLW2tiaiovmF8zq+5hGrSGTgwIFBi5xy6ahqduiYOlfSh65JHzddvxpryOEV1bIom83mTBhNG1OzDaPBqCqdUw6DcH/nUhMKqtnFFfq46aabAJdu6LeaUSNAOTLCpJ3O2dFoDzW71tRqDeVETVP7LIRJZsNoMGJJ5q5du+a82E/VLNbiRRJRxJnWUO48Ij9c1L179+B6lCziJ438+OOPJpk7OIVKW8M0mnYVhUlmw6gTqiq08GcizZw5M8/LJ8koT7TsDT8VNM4UilL4dnExktrV48xGqiVpTIFM0ms9YMAAAF544YWKj2GSuQ2TzIZRJ8SSzIZhdFxMMhtGnWBfZsOoE+J25/zD6uSqvPLDaY3mPKn3e6z3+ytGx6uwTokkh7EZRkfE1GzDqBPq+sscZ5CcYfzRqesvs2E0ElXZzGnWsSZx/lK53LVEDfs1QL4Yyibr3bs3ULj664+O3/LYyEdNOMrFJLNh1AmJtNptL3T+cquqaolf6aNWsy0tLUGbGg2m19jblVZaCXAthtSuVpJZA/PSbDOUFjvvvDPgWkA9/vjjgBvgpwaAyquPOxCvHlE7rHK/Z4nOZ04KLegaa6wBwH333QcQWc7oT+SLc09JxygVz/bVRxW2h1Un3aeuV69Rs4Wo5gtxaO84szazb7/9FnAzqX20YWkt1RN96623LvmFbo84c5xinmqxQgvDaDDaTTJLvXrvvfcA1yxg8cUXD1SyoUOHBr8D8sorde2Sgmor9PHHHwNtu73U3agdNKld3Vf1pU4ef/zxAOywww5AW29pzWxSF0g58m644QbAzWFKgvaSzNIqpCqeeOKJAOy+++5lvT88iaSUyl1ryfzss88GJlFU7/MFF1wQcFMuQ7PNYp/PJLNhNBg1T+dUSEISWVMo1KTv119/5bzzzgNcz+n7778fcO1qe/XqBeRPnNT/w4Xu2hHTDqP5O/OMGTMAOPPMMwE3BXK//fYL5jKrb/Zss80GwLrrrpvKtRXDbxFcDZJCPXv2zJvx9cEHHwDF55OF/y7Jtvzyy3PLLbdUfE3SAN99992c4xdD16ae7urHrtlnH3zwQfBcTZw4EYANNtgAcE5MaV/Tpk0D4IILLgBgrbXWApxWpjlqSWCS2TDqhJrbzLKl9txzTwDOPfdcIHfH1iymJZZYAnB2l3bGK664AoD55psPcJMtnn32WQBeffVVoLyEhLTtLe3yurZhw4YFLZS0K6cZhqmFzSzps9BCC+X9TSE5SaKTTz4ZgLvuugtw85xEoYiENLGoJIqk1lAa3Y033gjACiusAMBmm22W91rNE9ea7bfffgBceeWVQH4DS/lUFJmQ9nnssceW9IibzWwYDUbNJLPsQu2usp193n777WCki2KUvj2qlEh5vTXPSumPJ5xwAgAXXngh/fv3B6Ibl9fKE6r77d69ezARUfen6/7www8TP28tJHOhZ2jbbbcF4Nprry34Hkkm+Q/kD1FCzZNPPgm0SeXPP/+81PmrWkPFviVtpSk988wzgJs6qvXq3r17YBPrPZK8++yzD+ASgkSfPn0AF2/Xz3IwyWwYDUbNJPPAgQOB/FRE7Xarr7460GZbShKX8kBLIisVUDa1JkqGvaBpTRAsl/BoE99GVhz9rbfeyvl9EqQpmRWR0DppfvH3338feIJ1L5JcoembBX8vG7tfv35lX0fcNZTPQrZqlG9F0vfRRx8FYP755wfatAVFAZR6O2LECACuueaagsfSc67pp99//32pywwwyWwYDUbqkllSSDFIzS/2/15J/FfZXfIcyrbSLnj66adzzDHHAK6s0D9PrSRzofuMGgSQJGlK5pdffhmAxx57DHAzqseNGxdIXGXjaQzv+++/n9TpA+KsYSVDFpQvrtZT4Vz5pZZaCoDXX38diB6h89xzzwGuWCYOJpkNo8FINAOsubk5z/O89tprA/kS+cILLwTKG4geVdqoXVBxaGVUSQpPmjQp2FVrjbQG2X7KIjrxxBO57rrrAKettHcpaaUcccQRAMHweGkf559/PgcffDAAG264IRBfIqcVe4+TAabXShIryvLCCy8EMXXlNGy11VYFjyXtpRKJHBeTzIZRJ6RmM48aNQpwGTGSVML3YlaCaofllZQ3Vfmxt99+e7vXwsrDLsnUr1+/4N/a8X17OklJnabNrOvWsLyLLroIgDFjxgQVbNJC/IGBSVLOGlbil1C+9d133w0430ynTp1YccUVAVc3ENU8UpV/Dz/8cOzzi3Jt5sS/zP6XVCryV199BTh1WwUIRx55ZNnn9/FVsW222QaAm2++GcgNOURNaWyPwnb/wSpVfFANtSyBvOqqqwDYfvvtgzWR6aPmA4sttlji5017DQv1K5PjS/cVtXZKRf3yyy8rPr85wAyjwUi8BFLpk1Kro3amqVOnAq7MTM6gcojq7FgsRa695yaHpbEkscri/Nfo/mrRkqYalEQhB5gKY8Cp1fr5R+7CqWuXJtjc3Mwrr7wCuHRVmZVKellggQWA6iRyXEwyG0adkLjNLOly+umnA3DAAQcAzq6VY0S285gxYwDYddddgbZCi6iCA4UD9HeFnfz+0u3Z0E9cdtllgCsGUYhimWWWYZFFFgHgk08+AZz00meXhHNQpGkzzzufr2/nAAAUYklEQVTvvIBLDNHPX375JWhGoNCkpPeDDz4IwNixY3V9VV9Hew6O0/qq+EdrqM9Cz2Yl6DlobW01m9kwGonUQlOhXQVwUkYSS22CZHt89NFH5ZwfcPa4JJyK/GVrqgHC9OnTSx4zrV1dbYLOOOMMwLURWn/99QMtRAXtSnXU/Wy//faAk2rVkIRkVpG+2t/GQX4MlcBqDVXwf9ttt8U+pk97SGY9a4rSqIGjkLaloo5qMG+2YTQYiUtmP6VSLYCUvim79+ijjwZc2Z8vyYuhMjK/fal2/UrLy5LY1dWwTbZh6DxAW/qfPJxqO/viiy8CLilBKYIXX3wxABMmTKj4epKQzIo4qPWP1rIYklwqTtAEC2khUc0pKqE9JLNsZDVhkDYYuo7EzmWS2TAajETjzIsuumhgv6666qoAnHXWWTn/lzRSSqMkVjGJLJtNheDbbbcd4MrKNt98c8B5FsshjWwriJ4DFbb3hwwZAriyOWXJKa6sY/jN7mpBS0tLsBaKOCgHQC1yJFV13YW87r6kUvOJ8ePHp3DVtUd+Dx/dZ3tgktkw6oTUmxPI86xdXTaz/l8IP+daDdFlCytzSvZKNSRtb8mO9wtLCiEJqPuV9NZPZRWpWKESkowz63oVgTjqqKMAZxfvtNNOQNt1+0PxdE/KjFKMPQmSXsOo8kvlNYSzCTU8QPeuEUNJYjazYTQYqUtmxVeVs622pSodK8bKK68MwJQpUwC3U6rRmprkFfNelyp9S3pXV8XW8OHDI18ju0rtdpQdptxmNYsrx7Nfimoks2xjxUwVcdBnKgm9zDLLAE5Cf/fdd3mjWxXzlx2eVtPCNNoGibDWpTXTZ6QZ27Vo/RSFSWbDqBNSGxyn2Kk80M8//zzgYpaSShqort1+8ODBLLvssoAbXSP7U55yebNVPxtFU1NTKiNfiqGaamkiyhCaPHky0LZza4iZNAwNBlCtbxISOQnUrD/K/ldNrySWJHePHj0CCXXQQQcBrnFBrdfDJyw59cxJW9TaKTdCtfbSshQ9+frrr4Nm99KyOkLrJ5PMhlEnpG4z33nnnYBr7ObHd/3c7ebm5rw2OhrkpTznqFhuISRVoqRde1bc1IIkvNmlGtP7Y4S6dOmSI6XBaShpEHcN9XzJ466uNxr/qpwI5VX7UYbHHnss0CiTHIk7cuRIID96kUrboKampuzvBy/7PUKLq8R6OU0UqlAS/+KLLx5MotBDpIIDpTdef/31QHTXzjjYl7l81l9/fQDWWWcdgKADpzZMJYrIEVYrql1DTRQ57bTTAPdlHzRoEOAabOi5D0/sSOJLXApzgBlGg5G4mq0iiKjyQ+3e4akAPtoZVeCu5HwVfEeFm1Tk0Z6FFh2NJCSzwi9SpzuCsydM0muoZ1Qmgp7V0aNHA22NFeTErAUmmQ2jwahIMsfpQRzVfC8NKumNbJL5j0+t13CuueYKwom1wCSzYTQYqYWm/GT1YcOGAW6mckfBJHP5pDX/qVo6whrWcpJnFCaZDaNOiCWZDcPouJhkNow6wb7MhlEnxKqaKhWaCg9bV+Bd/YS/+OILir03jO9oiZrwoGNptGuh1Lqo+U2FpgVU4jwp1ymkSjBVk4UplT9eDX+U0FQ1DqS0HGBpOrXKOXafPn0AeP/999PPzVYbFZU5zj777EHTc/8B1Xv0pdKXwH+As9ls0HRAX07/S60NQx+I/u4nxHfr1i04hn5X4L2RD4J/D+HNqiMQLrSP2hD+KF/maugI3my1tipnmIOPGjaoGEXCSTnu5s02jAajqjizLynDbVqLHAMoPmC81EhTf0p9lIobllxR6nAxyZwk0mJ+/PHHoi1qk8Ykc/JU03oofAwoT4U3yWwYDUYsB5jfEta3P8Ov8aWObwf4Uj38vlJDxv33iELSXv/Wawtds1BTttdee63o+QuhweNqIatBafIn6GdHy55KEmll5bQZ/iNTjjR94oknANh0000B1xpKtdNvvPFGyWOonVG5mGQ2jDqhIpvZl75hKRd1vDh5vXqtaqPVdNz3JMu21jH1s5Bn17+ucrzZHZ1yfBRJ2sy+nSdtS+u19957A3DJJZcEmohYccUVgeixsArDqIY9DoVsZl/zqxWSxLpfcdxxxwHus9PIn969e5esKEylbVDPnj2z4FQGX60tdCw/FlysKYH/HuH3WVLoSv2ci93DPPPMA8Dnn3+ecyx98X/++efgZAcccEAW3JTDjhSGisK/P58kv8y77LIL4Dpt6gvz6aefAq4FVDH0uftmTzWqeUcITen61dlTk1ui0HdoypQpwdy1448/vuBrzQFmGA1GIiWQYZVGjc40g9ifOSSVohJHkKTqXHPNBbi+zmpR5EupsWPHBk3n/JZCku4//PBDVbt6lFYSZYrETNLJOcZJJ50EwAknnADAo48+GnSJjGrHVIlkjlJRpQnps5OTZ5VVVsk7RrHQYxj13lbSxd13313q8gqdq+aS2f+MRowYAbhmk6XQmra2tgbrvNFGGwFw33335bzWJLNhNBgVpXNqJ/FTMrt06RKElRSa0aypoUOHAvDAAw+Uf3Herq5jKoSkY2vOcSjfOniPJJb+JmlerQPMl7TVhLXKPZc+b93Tjz/+GEzO8EMdhXLPfz9W4pJL0umvf/0r0Dadc7XVVgOcHT1t2rSC741KDY5DMcmcZEOFk08+GYBTTjklmHahZ/LAAw8E3PMl5205yBckLeW9997L+btJZsNoMGJJ5i5dumTBpVlKYhQKBS2//PKAs1U1p0c7l3ZzSRDZ2jNnzgzsbeFXYr3wwguAm1Ol3VxTIx955BGgLXQl+07hLVVxSaqHJXNzc3NW1xCmWFXTXXfdBcDGG28MtNnp4GYsaR6W5veWIyEUctN96/861yabbBIcS7am5nD5tlwt0jk1R2y55ZaLfI2Sau69917AaTLyjJ9yyimAa6cch3Js5koktJ5vPY/y1XjnzjmufEILL7ww4LzbmiOmyZl6Djt16sStt94KwOabb55zXh3bJLNhNBixgnu+h9KXcrPOOmsgEdZcc00A+vbtC8Czzz6bcwxJWY0EUcLB//73v2A6oh+D9Gcayfunne3hhx/OOUevXr2CeUGSzCrRLIS/axeywYV2ekliMXXq1JxrvOSSSwDYY489AJg4cWLJJAHN0tLsI5XGSfOQjfXhhx8Gc4mOOOKInPMmieLLl112WcG/F5PIsoV9iavPWlK9EolcDnFqxddbbz0AXnrpJcBJVWmXYXS8pZdeGnC+EkVchL4jeialoa200koAHH300UHERVRaxGGS2TDqhFiS2ZdcslVlS/z000/sueeegNvVJDXFWWedBTiJprQ37UbHH388SyyxBODsQGXTnHfeeYCL5clO1RR72dKSTh999FGwM2snfOaZZ4DC8c8CtgqQa2/pWlRQLhtwwoQJAOy11145x7z//vuBwmmM2vEVv9Xurni5dnndg7q1iL59+wYajl/YkSRRErkcpBH5yGbWcMC0iNO9RfFd/znXc6ioyZAhQwJ/jaacbrnllkB+vN+XstLKpO306NEjeL4qGa8UxiSzYdQJFcWZfeRt3Xjjjdl1110BOOqoowAX/9Sus/322wPwzTffAG6nWnfddYG2WKVsVXnNtbtKCmpes1q0+FlDen1TU1OwMyoTTVI71M6opDc7XL6pndf3XupaJUV1f9IyZKuHj6Fr0U/Zj4o3aoeWFlDJiJ/2ak4wfvx4APbZZ5+Cfy+VGRaHpDLAtKa+RqZ10f8HDBgQvGfnnXcG4Morr8w5RhT6rjz44INA2zOt+dBRdQvmzTaMBiORtkHaseacc06GDBkCuFivXiO7UHFFxX/XWmstwEnXQw89NJBikkSyr5SDvcgiiwDOHtVuKM+1Ytm/XzPgdlvZoypBK5YBVihHWSVuGrMjm0lxdX8wt67tqaeeAtpspaeffhpwElfH+Pvf/w64CIBi1DpW7969gTYvdrm0h2Tu3Llz3sB1aWjzzjsv0PasJEUcydzU1BQpPUPdMP3jA0676t+/f+ALKNVIIwr5XqZNm1ZSSzHJbBgNRty+2Tk//fY9M2bMCDyC8sTKdlQ8bvLkyUBb/i44b/agQYOAtt1P2WDyIiq+KskrD7lasCjvV9KgUIWS7F5VVBXbDaPaImUyGR5//HHA+QQuv/xyADbccMO8cwL069cPcJ7QXr16BTu8srdUD6zMLsWmZV9FXXtHHS0UlspXXHEF4HKXlZPQXoSlsnLJzznnHMBpT0IalJ5NZTF++eWXvPrqq4DTGstdE71OmuoOO+wQ+Zq46xtLzW5paclpgi/CaYdPPvkk4EI0CsDLAaUQkT4EqYxKVcxms8FGoIdCKolCM1I3V199dcA5xJRcoXsq1OdaarCOFdd5IvX65ptvBpx5MGbMGMCFKLTB6b61EQ0YMIAdd9wRgMMOOwxwxSeTJk3KuR/dh8wGOUiq6eyYpppdqDebTKOtt94acKZRklTrAFNYT4k36goidF9y4oaHLWh9tTZKElHCT6GhB3ExNdswGoyqJlr43TE7d+4cJNRLusrxIVVF6kmo0AFwzqZsNpuXNqqUzylTpgAuNCAHmVR7qaU6RzabjSzaF3F29dlnnz04jkJbun7t0HJevfvuuznXKDp37hw4TfzWOaeeemrO/eoz0ueghBw5EcN/i1rHWkhmaSsyE8LXpXWVqSD1MkniSuaoogt9htLwVMbo38PRRx/N6NGjc96jEKQKLPbdd18ARo4cCTjtqtDkCz23UaFHk8yG0WBUJZl9h1g2mw1sQyU+qPDAH/7mz0gK28n+fCoVYUi6K1n96quvBpxD7Kabbsp5X6HJA3447Zdffgl2PfkEoooVOnfuHOyisv0feuihgq8tB12D7Mrbbrst5+9yjvgNDMP3UqqwIg3J7GsUPq2trXkzxXzbMknC9+g/o3GQr2KrrbYCYPfddwecA1Z+oEwmEzyjw4YNA5x2Em4HBC4UqoYDarGk1N1ypmOYZDaMBqOqvtnafbULzTbbbHm2sF6jYgwlawjZIdq5Z8yYkWdvSdqrBa4STWSfKCx0yy23ALm7sm9T+hpCsaSRQnOxkugLLhTe2m+//XLOM3DgQMAVKSiZQXZ4sWv0SUMyK7Qmz20hpHHVol1xtd7sFVZYAcgvhqkkRBSeGBpGNrXSPqVl2qwpwzDySKTVbrgAXBLKL3BQzFjePElkFRNIYmez2Zz5ygCDBw8GcksbdT5w3sdwGqdPVOvbuA39Sk1KUMtVxaELvU6fiext2VG6NtnIuv+oMsJySFIyK8ni7LPPLvq6TCYTNPLT2qjBu0pf/dZQShTyNTcdD9xz5nt9w/c422yzZaF4GaEaJkhrVGqpbOZwIUVcFElRo0UVUSi+rvlRKuctB5PMhtFgxJLMXbt2zWno53ukZ86cGdn03behtHMpqT1cmuh7ydVQQJ7Dzz77DHA7bDHJ5cfw/P8nVT7nX7PfhjisvUgyS2opA0kxeqV3brbZZkB+g4c4JCmZlfmk2L2PbM6VVlqJt956C3CZUEIN7ZQpqNi51kOaTZx7jrOGP/30U5DJpUYOar00fPhwwPl3tC7FWk1JU1NGYfi7AM7fo0jLqFGjyrwrh0lmw2gwYhVa+LaKL30ymUxOJlf4p2876j2S8oW8wJJy2s1kZ/llllHvK3TNxeYzl4N2Xv3028NEaTrhhgnzzTcf4DK5dtttN8BJZF1bKelUTowySaIksjj//POB3LVUBEJNCpQbv8UWW+S8V9lW/vC5OeaYo6hkjEu44Z5KaZUTL9tZ/h3l0KsWQL8fPXp04MmXDdyjRw/ATbHUe7bZZhsAbrjhhsTuIQqTzIZRJySeAeYfz2/XE5UBVqjxmmwW/U3F/PJUasf2W+Jqxw2P+fBtebXpveOOO2o+dEw2mTyf2t015rMadH/ykD/++OMV28zSIOSj8FsjCbU4lsbUtWvXoOHdNddcAzgpp+o4aVmS1D5xGhRW6veYOHEi4Jrr+eiZ1bWERxv5QxyEnrPHHnsMIGijq2aTlWA2s2E0GLEkc6dOnXIks3Yh2RrTp0+vunA+bAfKDlGBv5oQaCiZMr4k/eXdloT79ddfg13VRzt+e4wDldd03LhxgMsOSqNwPwlvtgamXXzxxUB+2yJpQLofFfv/fj5dR9zTlk2cNQw/X0lcky+hFXlRhlcSmGQ2jAYjljfbt2sLZeT49nTU7hf193ANsmxj2ZSSxKeffnrOexTLUxw07DmXBPa9176tUysymUwQe9V1qvZVGo5ytuMeF9KRgMrekq2vc0XlNIfpaK2Nwtcjz3a4c0ghlJUmn01zc3PQYUYjbRTVUMeZOEgDVYutSqmobVBUoXkmkylZHlfygjKZIJ1RNykHjCZYaFqGpiEoEcQvPyvkkCuQ1BF8yzXl0u9nnSTh+9P8LZWLanOstONjIdqrb3YtScpUUnhMwkM9vyRUFF4LN4fQmmkjViJQkpiabRgNRkVTIKVSqGhbDQggP/kjKjmjmEYgtUeqsBr6KYSgWb5+Ar5CB9Icfv755zz1Uyp8IdUqSYkYRSaTCT4/P6FfUz2kwvkN8vyy0komXBj5n+cZZ5wBOHNCJafqxOq3xwojk0jPpI+cs7V4tkwyG0adEMtm1iwmJar7oZSw279QYX8Y/7zh3dKf+yx7JBROAnIlcPgc4SZ+mliv+b+S3pJu4bZBtbIno4pQfAdjeMZVpecIl3j+/nuzmduRcqau+JjNbBgNRkWSWbaGpGnYDvWbEshWkJTxPc6+9zvcpE4SS8fSe1XIrkZ4kuD+rtfS0pJXgqhjhxIHEt3VK2kfFHUMoWMppKVJH+WQpDd7nXXWAdJpZF8NcdcwShNS6C2JtNokMclsGA1GLMlsGEbHxSSzYdQJ9mU2jDrBvsyGUSfYl9kw6gT7MhtGnWBfZsOoE+zLbBh1gn2ZDaNOsC+zYdQJ9mU2jDrh/wF4kf0OI83oZwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5422ef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "# Make the discriminator\n",
    "D = discriminator().type(dtype)\n",
    "\n",
    "# Make the generator\n",
    "G = generator().type(dtype)\n",
    "\n",
    "# Use the function you wrote earlier to get optimizers for the Discriminator and the Generator\n",
    "D_solver = get_optimizer(D)\n",
    "G_solver = get_optimizer(G)\n",
    "# Run it!\n",
    "run_a_gan(D, G, D_solver, G_solver, discriminator_loss, generator_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Well that wasn't so hard, was it? In the iterations in the low 100s you should see black backgrounds, fuzzy shapes as you approach iteration 1000, and decent shapes, about half of which will be sharp and clearly recognizable as we pass 3000."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Least Squares GAN\n",
    "We'll now look at [Least Squares GAN](https://arxiv.org/abs/1611.04076), a newer, more stable alernative to the original GAN loss function. For this part, all we have to do is change the loss function and retrain the model. We'll implement equation (9) in the paper, with the generator loss:\n",
    "$$\\ell_G  =  \\frac{1}{2}\\mathbb{E}_{z \\sim p(z)}\\left[\\left(D(G(z))-1\\right)^2\\right]$$\n",
    "and the discriminator loss:\n",
    "$$ \\ell_D = \\frac{1}{2}\\mathbb{E}_{x \\sim p_\\text{data}}\\left[\\left(D(x)-1\\right)^2\\right] + \\frac{1}{2}\\mathbb{E}_{z \\sim p(z)}\\left[ \\left(D(G(z))\\right)^2\\right]$$\n",
    "\n",
    "\n",
    "**HINTS**: Instead of computing the expectation, we will be averaging over elements of the minibatch, so make sure to combine the loss by averaging instead of summing. When plugging in for $D(x)$ and $D(G(z))$ use the direct output from the discriminator (`scores_real` and `scores_fake`)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "def ls_discriminator_loss(scores_real, scores_fake):\n",
    "    \"\"\"\n",
    "    Compute the Least-Squares GAN loss for the discriminator.\n",
    "    \n",
    "    Inputs:\n",
    "    - scores_real: PyTorch Tensor of shape (N,) giving scores for the real data.\n",
    "    - scores_fake: PyTorch Tensor of shape (N,) giving scores for the fake data.\n",
    "    \n",
    "    Outputs:\n",
    "    - loss: A PyTorch Tensor containing the loss.\n",
    "    \"\"\"\n",
    "    loss_real = torch.mean((scores_real-1)**2)\n",
    "    loss_fake = torch.mean(scores_fake**2)\n",
    "    loss = 0.5*loss_real+0.5*loss_fake\n",
    "    loss.mean()\n",
    "    return loss\n",
    "\n",
    "def ls_generator_loss(scores_fake):\n",
    "    \"\"\"\n",
    "    Computes the Least-Squares GAN loss for the generator.\n",
    "    \n",
    "    Inputs:\n",
    "    - scores_fake: PyTorch Tensor of shape (N,) giving scores for the fake data.\n",
    "    \n",
    "    Outputs:\n",
    "    - loss: A PyTorch Tensor containing the loss.\n",
    "    \"\"\"\n",
    "    loss = 0.5*torch.mean((scores_fake-1)**2)\n",
    "    loss.mean()\n",
    "    return loss"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before running a GAN with our new loss function, let's check it:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Maximum error in d_loss: 1.53171e-08\n",
      "Maximum error in g_loss: 2.7837e-09\n"
     ]
    }
   ],
   "source": [
    "def test_lsgan_loss(score_real, score_fake, d_loss_true, g_loss_true):\n",
    "    score_real = torch.Tensor(score_real).type(dtype)\n",
    "    score_fake = torch.Tensor(score_fake).type(dtype)\n",
    "    d_loss = ls_discriminator_loss(score_real, score_fake).cpu().numpy()\n",
    "    g_loss = ls_generator_loss(score_fake).cpu().numpy()\n",
    "    print(\"Maximum error in d_loss: %g\"%rel_error(d_loss_true, d_loss))\n",
    "    print(\"Maximum error in g_loss: %g\"%rel_error(g_loss_true, g_loss))\n",
    "\n",
    "test_lsgan_loss(answers['logits_real'], answers['logits_fake'],\n",
    "                answers['d_loss_lsgan_true'], answers['g_loss_lsgan_true'])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Run the following cell to train your model!"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 0, D: 0.6062, G:0.5067\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5a77198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 250, D: 0.126, G:0.4086\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5a80b70>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 500, D: 0.1187, G:0.3065\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd56fba90>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 750, D: 0.1664, G:0.2642\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd7628a58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1000, D: 0.191, G:0.2651\n"
     ]
    },
    {
     "data": {
      "image/png": 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YZDaMEiPvkjlaKZhcdgrMh76Vy17F+ZLM+RpjNu0ekc9KsuOLJbFTwSSzYZQYeZfM+SYfkjmXFIrOnE0KaQ7TKdiQCJPMhlFipCSZDcMoXEwyG0aRYA+zYRQJNVP5cL6NC9mgkIwn2cAMYIWJeq0pzDceJRmbnQ6Z3gjZ6Kkc5m/Yw5w6uZjTVDBrtmGUGClts43qhLl6x2qrUygSolTI5HrnMvLNxySzYRQJJplzRLwsGa3iyTS6ywdh6JCFpoemQzSpqxLBKq2bD4ksTDIbRpFQMJJ5++23ByoLoqlpmKpQNG3aFMhuRY5IUsk19T+74447ApVlfsFlg6mZfNeuXYMywypPoybrarouVB4pmbI1uUStZfzie2qeduSRRwZtW9TQ4MknnwSgV69egCu1rAZsYcYyQ2Y7AeWbq2yuSvNedtllwTF1fI1L5YF80smNTpecu6b8royqZrh06VIAli9fHnQF0ATroVZPKT34H330UaanE5pbQ5UeNR6/w+Ann3wCwBVXXMFVV10FwN133w3AUUcdBcAxxxwDwIABAwDXacHvIJgK2XBN+ZVUZbhTbfL+/fszbNgwwNVp09ypnrq6SbRp0wZwN71qV6dCWHOo66zx6Hr7D2RZWVlOH1JzTRlGiZGzbfYLL7wAuAr/48ePB1yl/7lz5wJVOxq88cYbgDMyaFuqjoFa7XNZhsdHq/mQIUMAuOaaawD4/PPPAbd17tmzJ1C5ZdaY1XdoypQpgNvWLV++HICTTjoJcJ0zk+lLlE383soqLCHpqx1VixYtAok1bdo0APbee2/Aza++o2uhbg4HHHAAkFtDmTqR6pxuuOGGKu9rLOrKEvlaIWGS2TCKhJxJZkks1SI+/vjjAWjbti3gVmjVHQa3Snft2hVw9bHvuusuwHXJULcIvX7++eeHcs7J6EUaj8rnqqdvhw4dAKcTKga3VatWPPDAA4Bb6bWzkGFJOpt0U0lqfT6yt2+mpGIoitQZwRn3/I4cffr0CV577rnnAGcIU0cHIaOZukbIPadrkKmEjlaWykc9pFTHXKjUj2w3MsQmg2/gEzqWihiEiUlmwygScmbN3m677QDnXI+n76ovz4UXXgiE77aIJFVLaKyyMHIvyTagHkPqKBh5nWXRV48hSTwVxVdnBVl2pcvlO9HCn7NYUr1OnTpce+21gLMZqE+xvqO/Ktqo0rvpFMVPdQ4bNmwIVO8MqnnQ7kE9pVMJBMlGcIxZsw2jxCjoFEh1o1+4cCGQ/S6JicZXo0aNYPWWTicpKr1WklsSRjp15A5Eq7eCQqQvfv3111Hf1y4gHQtqqpI5Wp+odNAuQ3Mo/7J0Yl0fBZdoF5LLTp7+b8lmoYAfnZt2RvEktHRj6cpi1113BZxNKB1MMhtGiVGQklkroPzK5557LgBjx44Fwmk2JlJd1X2dSDqfpKZCHGNJ0cjVXau2kBSXz1XSXlbWRH18o5GvLpCKZrvxxhsBeO211wDnS5cunag7ZDKkO4fS05s0aRL1c/IaxLOE++y3336A88rII6F7WTu5VDDJbBglRsFJ5iVLlgTxzb7lMxtkGtfr9/D145Z96tevH+jChx9+OAAPPfQQ4PRtjVe6WjxiSTSdx+rVq3MmmbWDOPfcc4OIN+1c2rdvH/d8MyHTOdS5+LuEdO47HUPSPJOYAEVAduzY0SSzYZQSBZMCKZYuXUqLFi2qvKYVX1bdfCXxR1p6Jfn8c5FEjhU9VqNGDfr16we4yCNZ60eOHAk4n3zk74Jb9SOPHUvChWlXSBbp9C1btmSHHXYAnGRWlpTioPv06ZPz8/Px/cy+FyEZpBs3b968yuu+RNZ9oLmbOXMmUBnlGGsOO3XqBCS/izHJbBhFQsHozFqxxo4dywknnAA4PVS6YzZavKarbyWr++lzip667bbbgtcUDbfVVlsBTkdSwrs+l0mGTi6t2a1btwbglVdeCaKsFE9+wQUXADB58mQg3N1VunMYK285HfwdmXRm2UXmzJkDxC7sEI+1rm72okWLANhyyy2j/W7Gx48VwhfWw6xttx48/7pecsklQGXxAhUj2HTTTQHnrtCNoMINsYi2hVeiR48ePap8NpcPs1I199lnn+A1natCWPXgpKMGKPFBBR9EunOoc5CwkDEz4lgJj6F5vvXWWwG3NT777LMBGD58OOAeYr0fDT/JJOI3zABmGKVE3iWzVu6TTz4ZgNGjRwfvqZDBO++8A1RPJwuDbLc2UTijgigGDx4cjFk1pUaNGgW4rZdWaElereoKPIisr53IFZYLyaxz6NixIwCvvvpq8J6vImWjnllYriml0voGWH8+6tSpE6Tlzpo1q8oxJM0nTZoEVNZDi3xfcxmZrJKo1rZJZsMoMfIumaUvSuqsXr06WCFlGIpFGEXVUlnV69atWy1lU7q4Vm9JHl3X//znP4BboZcuXRqU/5H7RvjuC10b6dC+QSxyVZfkUyHBaONLZozpcPHFFwNO5xw0aFBwXnpN//dTR1MJIomVfprKHPbr1y8oCKmqm/ptpWnqfc2DxqBx3nLLLcHxFHrruxO1E1NxBBVpUEKJXHWLFy8O5jNW4QKTzIZRYuRdMvsrc926dQM9M4xSuonIVN/S6q1xKPVPZXLefPNNwBX4KysrC9wU0pUl7X3dSYEI2qlEnGeVz8cjG5JZOqXKI2s3oDTHTp06Bfq/ii/qOhSSzixpettttwHQvXt3wCXAyFbj8+OPP9K4cWMAXn/9dQC6dOkCOBuB6oOrLJKvDyvxRMUq42GS2TBKjLxJ5lhW2MgVLNG5+YUAcpnY7ktHFSiUdFIHCyUYPPzwwwAMGzYskLQLFiyIemxJ+86dOwPwzTffAC5pXgEoK1euTFhuOBPJ7I9xt912A1wRhf/+979AZWF/cD7VioqKQPLI6it90J+jZHYZiT4TlkdCx9fOQ7EPus/kcZk/f36Qwimfdyz7jUpKy2aiwo677767zjdhkT+TzIZRYuQt0SKOXzThd6XryJKYy4LpkhJakRWVpFDMQYMGAQTtWe644w7ArerNmzdn8eLFUY+t3YqixWRdlUTW6i/LP2SnAYDGqOss24WS+TVWSRRZhc866yygUkJ/9tlnVT4TR6omPJ9sza/03Jdffhlw45Y+LEkZLTVSnxG+RPbTKoUiHCdMmBC8FlbZXZPMhlEk5N2aHQ9ZSRUJpoR3pQ5KkqktiizIqZCpvnXKKacAzo8oKfXBBx8Azlesc95rr70Cn7Tid9VATpZwpeCpOJzGpRU8mmSPpVeGYc1+6qmnABg4cCAAjz/+OOCa5CmZYttttwUqfalqBiA/u1ICtSPTX/+8Y/mSkx1jmPeoUm81X9oZJZMkonO87777AILOpooMSyXfwHRmwygxci6ZY7VjldX3vffeixkjKx+efJWKesplMTi//K12Bfvvvz/gsoPkg5V+qb8XXHBBIJUuv/zyKucvX6ysxsqu2nrrrQFnI0h3fH//VsIxSiLJVywpe/vtt1c5X0VEaT6kB1ZUVATWfPlsTzzxRKC671bXMZNovmzH16eCdiv33nsv4Bo5PPHEEwDsvPPOgCvWuMEGG7Bs2TLA7cx8TDIbRomRc2u2pIt0TDVWi8xj9iWyGq3JQuq33MylNdsvsiedWLsJ+WClO0sKKya3SZMmQZtT6Zpqrq4Vety4cYCTkGpxkyv8kr4ak8rkan40LyrwLqk6ceLEQCLJB62difAbuhVii9R00H1wyCGHAK5t0T//+c8qn9OOrqysjB133DGU3zbJbBhFQs50ZulZWrEkneQzVfbR6tWrgwoc0tkUvypproZeZ555ZrqnE5BpEXyh81dOr6y4akur8a633nrBmP0qJZLiimdWsbhMfMlhxmYr00fn9e9//xtwednSF9u3bx9I61j3V6I87FQoJJ1ZaNwan8abZvnetatsUCTqXNG7d++s/1auboTImzee+yVsclk2KHKM2aiPHYtCeJj98aof+XXXXVflc82aNQNciG4ymAHMMEqMgpTMuSTsVT0diZROxcZkyUevqVxTCJJZ873vvvsCLkQ0pGObZDaMUqLgOlpA9cD2tYlkJPIPP/wAuL7M2ZDI+SZW2dhiIYy+VGFjktkwigTTmQtA38ompjOv/ZjObBglRkqS2TCMwsUks2EUCfYwG0aRkJJrqtiNC8U+Pij+MRb7+OJhktkwigR7mA2jSLCHuYCYP38+8+fPz/dpGBlQs2bN0Ernpoo9zIZRJKxVEWB+K5Ywso1yZTxRYfubb7456e8kaj2TDGYAW/sxA5hhlBgFI5m32GILAPbYY4+gHGmnTp0A15RcReHDJFeruvSoF198MWimphY3KqmrgulhYpJ57adgywZdffXVgOuOd95551X5W15eHpSfEX6trDBDUMO6EVT3WXWhtSCdcMIJgOvT27lz56A+mLbRyXRISBd7mNOnTp06QHZ6SqeCbbMNo8TImWTu378/4Loi+kXt1LPpoosuCrpeyLClBHd1k8inZFbHit9++w1wElnjSCZJXd0MpDaoZrSOlUl3B598SeZjjjkGcJ0csknYkvnDDz8EXPcVoR3UtGnTgvGFUV00ESaZDaPEyJpkXrhwIeBqLbds2RKABQsWxP1eRUVFIN0k/VSTOhvpmpmu6urQ0bZt27ifk971559/BrXDX3nlFQCefPJJAI4++mgAvv/+e8BJCPWxUr+mRx99NOnzy6Vk1rxVVFRwzz33AK6Tg+ZOXRCPPPLIqMeI1ossUY3tsCTz3LlzAdf3LB7ffvst4Ernyv6hHZq6gqg0lDp3qGa6OqEkg0lmwygxsq4z77PPPgD8+uuvAMybNy/u51u1alWtt9J2220HpNcFMRGpruqycKrnlPRa9ViePHkyUOmCinzft9BHQ7qybANhWFFzIZk1Rs2tLPnRUJ9i9WAKg7Aks3pIy1bjB+sMHz4cgPPPP7/adyN3JZH/DwOTzIZRYhRM0Iho3bo1H3zwAeCs2Frl1I955cqVof1euqu6Ojjqr3Q96bvDhg0DYODAgUB8ySw9KxsB+tmUzJ07dwZg8ODBAHTp0qXaZ6Tn+mNTkJCun3TKdEhlDmvXrh1YpWWjmDp1KuD0Wc2HwoWF37c6Eu2itHNr3Lgx4O7VTHZZJpkNo8QoGMmslfuOO+7gnHPOAZw00+qtnrZhkqpklmVdqYpXXnklACeffDLg2pMIXyKXlZUFll2t3j/++CNQfeXX59TD+pNPPkluUBGEKZk1Fllo1be5devWAHz++edApd6YyEag3ZbsBH63xEjLdbt27YDYNpN0d1d+rEOsZyGe/utb3zWHiiWQpM4Ek8yGUWIUTHsa+fj8qBvILAUwk9ai0b4rq/wtt9wCwJgxY4DqUVybbbZZ1GOOGzeOPn36AE6/1nekc6oPtYgnkXUeF198cZXXd9ttt5jfSRdJS8UQyB6wZMmSKp+TzzUaiqCTN8DXJaP5kjPxYhx44IEATJ8+vdp7mrNY90YyrXX8uImffvoJgNNPPz2l84xGz549U/q8SWbDKBLyrjN369YNgNmzZwPRrX7ZbMqVir5Vo0aNYDWXROzXrx/g9C9dT0V3KY1Tq/y6664bSOBnn30WgAEDBgDQt29fwEWTHXfccQA888wzgLO2yg8t6Zbs+JIZYzwUMzBnzhzARa5Jgkg/jIwp92PZv/vuOwCaNm2a7mlUI5U5jLRZCM2dfz39nVm0+1DvbbLJJgAsW7YMcLq/0nfff//9pMYSDdOZDaPEyJvOLOmi3N7NN988eF0rpOJ3Ff/6zTff5Po0qxApcWbNmgU4qSoJJAu19EhZv4cMGQJUlg169dVXAWjSpAngdGeh8f/8888APP3000Biq262kUTedNNNAef311xGQxJZZKPARCpE24kqzlqMHDmyyv8lkXX/tWjRotpxJJGFdH+NX9cqm+R8mx2rbpeC21966SXWX399/R7gDESZJFoccMABAMyYMaPK6+m6NXQuMs75Rh9VEdEDqYWgQYMG3H///QD06tULqL5tlqtKx27evLnOL+b5yCWiAP9o4/v7GGlfxEWLFgGw5ZZbAm5e9NtKomjWrFlgyJSbUYkXSok89thj0z2NamQazin1ReMQWphl1EpZR5jQAAARXklEQVTn/lOASiYuKttmG0aJkXcDmE95eXmwRdH2UqV4brzxRiBxSlwqZLqqKy1RoYGq56UQwcMOO6zadyTRZARSmqhUjlgofFLb3Xr16iUsOZTNcE7fHScpPGLEiCCoRsY8v+STvhMGYSVaKHgkmaSYVPF3lyojpbJS8TDJbBglRsFJ5lWrVgWuAt/Vob8KiJBuuXjx4oTHjRU8ksqqXrdu3WoBLLGun++C0e/XrFkzkACx8I8pI4qMbHvttRdQGbwhw4vcWn6Fz2xIZv+3pPNrXO3atatSXCASGQxVPioMMpXMF154IeCMcyo6GQ/ZN+IZ//4+NyCznYhJZsMoMQpOMvfu3ZuhQ4cCziUV53yAzKzc6a7qSmRX6p5WaOmwCvtUJ4t777034TG1E/Glvyzwb7zxBgDnnnsu4Czm8chXQT/Nia6Hn84a8m9lpdSubBiyVSjRZ8WKFcH45D6VjURz1KFDB8AF/PTo0SPm7yTqzGKS2TBKjLxJZl+qyne5cOHCQB9R0TNZrROF16UjocNa1eXf1ertlxVKhhYtWgAukSFWsQIld5x66qkJC+TlQzL36NGDp556CiAoASWfrf6GST46WvjlkOVXlxVf8+8X+ouGdl6KhfAxyWwYJUbBSGaxYsUKxo0bBzjd0JduYejKohD6FCnUT/7jRKVeU9E786Uz+/5vRY+pkEGYFMIcxroX5U9WPEKaxzbJbBilRM4ls19OVhFSt956K1DZ1uStt97S7wFO/1A64XvvvVflmJk0+MrWqi5L/MSJE4HK7paxkF7lWzP9CCulHCquOZm5y4dknj17drXifoWSxpotND4VklDBQs1trNj5ZDDJbBglRs4lcyx9V9Lnl19+CVYxSaYpU6YAsOuuuwJOMilmOxNytapHS+NMdO3lq7700ksBmDBhAuCyeMrKythvv/2AymyzaORDMq9cuTKIVpOP9rXXXtP5hP57+ZTM/njkeenduzcAjz/+eMJjJJLaJpkNo8TIewSYEt3199133+WHH34AXElXSeTRo0cDsPXWWwPhJHxnuqrHar8qm4CKE8hnvnTp0mqtTBIhS/C2224LpJYtlkvJHO9eks88Gy1QcyWZlRMwc+bMQJpqXJpL5UZLV1YUmZr/pYNJZsMoMfIumVVVRDHO5eXlQT7pgw8+CLgVcZdddgGcDzOMnNhCsIRqDvw8bWVLqdJIOi1cciGZFacuz8TYsWODnZYqrSTKu86EXM9hkyZNqpV68qMS/QZyO+20E0DQeikVkpXMea+brRtU25Zu3boFF0YPum5q31WztnLmmWcCBOWDNE5dgzDKJOWSadOmAbDnnnsClYkiqquleuhaiIuBli1bBr2zR40aBbhwTblJVdNOXT9ywdr9VBiGEZD3bXa+KYRtttxVkmzJFFtIlmxus303o3ouT5s2rdruSapSGO5En0KYQ/85sv7MhmGkTUFK5ngdBLLwWzlf1ZMdX9jFF/4+ZuhjVBldFfT7+3f1e2kdM5WxF/IchvRbJpkNo5QoSMmcSwpB38om+UqBzCWlNoexMMlsGEVCSpLZMIzCxSSzYRQJ9jAbRpGQUjhnsRsXin18UPxjLPbxxcMks1HylJWVVfEX+/9fW7CH2TCKhLxnTRmOMEsIFyqxijnkkyjNBNM+lsolKdMvl5hkNowiwSLASsx4UuxjzOb4Iksf+a1atavyyyLFaqmUynNnBjDDKDEK7mGObF49ZcoUpkyZQrNmzRK2d10bady4MY0bN6Z+/fqBrrU2M3ToUIYOHUpFRQWtWrWiVatWrFmzhjVr1vDHH3/wxx9/ULNmzZgN8cIkFYu0/9latWpFbaK+zjrrsM4669C9e/fgO7Nnz2b27Nm0adOGNm3aVPuOxh/r/2GScwOYKm5usskmQGWNZXAJ+W+99Ra77747AJMnTwZgq622AlwS/9puKNI1UMkk1f66/vrrARg0aBDgOnmoGukXX3yR0/NMFj2cF198MVDZL0zdH4WqVar3tLan+qs5VzeTTIlVLCDaPROr9rUeaBnr7rzzTgD69esXdGA57bTTANePWV1V1I/6448/Blz12e+++y7meWRKwUlmwzDSI2eS+emnnwbgiCOOAOCrr74Cqhfr0woNrgu9ag/vvffeALz99ttAdis+poskgOplS9IceuihAAwZMiSoYnn88ccD8OijjwJOIr/yyiuAq3bpG1kkCSW5c4X6Jy1ZsgRw3RpGjBgBOLeMOnFApZQGV8FTuxCh6p316tWr8jdskpGE/nVWv+RWrVoBrh9amzZtgmuh/mAqyqgOHqqoqvs7F0UoTTIbRpGQM9eUuiGqJ5J6EB955JGAW7EbNmwY6CzSO1ToTt0fVZ7mkUceAWDZsmUAbLPNNgAsXLgw6fMK262hzhWyBUQcu9pnJb3VOXDDDTcEnH6pY2y22WaAk3ipdIfIhmtK0lP64ZZbbgm4XkkDBgygX79+gCsfLAkWy/ilnVuPHj10nkmfT7w51O+pFG409BlJT3VKURFC6bu6z4YOHRrUze7evTsA22+/PeD6guk7qv3+xBNPAATfS6Ubi7mmDKPEyJlklmtJxdEHDx4MOJ3ixhtvDD57/vnnAzBs2LB0fy5pshVwIH1Wq/6AAQMAmDdvXrArueOOOwB49dVXAWcvUCF1vytCOhbQbAaN+B04IpG+/8wzz1R5XRbiu+66C4DzzjuvyrHSsYOkMoeREt+/nrruOoerrroKgEmTJgHw2WefAZU7qMMPPxxwjQy0I+vVqxfg+qL5wSTphLGaZDaMEiNv4ZySWPI3R2vjIeuodLFsBOensqrXqlUrpgVZeqR0Q/lTpRs1btwYcDuTSC655BIAbr75ZgA6deoEuM6BN9xwQ1JjiUa+wzmTTawIa/eRaHy1a9cOJK9vWffbH22wwQZA9TmN7Iem+7hJkyYAfPnll4C7H3bYYQegsrupvpsqJpkNo8TIeQSYVjStevEaa8nC2bNnT6Cq/zIfxPPrygc5YcIEAGbPnl3lO9Eksr4zcOBAwK3ykk6ZSOR8UlZWFsxzsnq//NDLly/P6rlF6uT+buGggw4C4P333wecJFbP5Ug/tCTszjvvDLhIL/mbdX9LIstzkc3xmWQ2jCIh6zqzrzP5rS791XHBggVstNFGgFutM2lx6uuyPvH0La2m0XYE/jnp/7179wYqexSD05EkmcrKyoIxd+vWDYAXX3wRcG1PP/roI8BJ9XTS53Q+5eXlOdOZmzZtClTG0I8cORJwcfXyx6ZDonHHm8N4Ort2D75tRvYN+ZX1XX/nBM6frIhG+aaXLl0KwA8//BD33OOhOItVq1aZzmwYpUTOrNmxVsjWrVsDBFk2vXr1CqSaVk758HbccUfAxWyHQVh+ZsWPy7+qjChJV/lXt99++0Dy6tr7u4c+ffoAbvzyWaZDLvzMvnTq3r07Y8aMAZy3Qp/V30Rx5WE3jot3PL0nfVc7MX1WWW6K3qpbt24gNXXfduzYEYBFixYBTtrrflcEmr9TS3V88TDJbBhFQs6s2bH8jAsWLACgZcuWQKUFUVJc+cuyJipaKEzJHBaPPfYY4HYRksx+Tuwvv/zCnDlzqnzX1+cfeOABAC666KLsnXAISMoo8+nAAw8E4Lnnngt2H5dddhngrLpdunQBEkvmXOaq67ckkSU1NWeSyPL7b7TRRoGlW1lxir2WtVpWbuWgK4/Z/80wydk2W0kESrTYaaedqryvbYjvyAdnWOnbty/gUgXDINNttowi2irrhpARRQa/uXPnJn1M3RBaEGRESuY8/ISCXAaN6OavqKgIkkRkxNQDou1porDNaNtQLXp+mmS6c+inJSoppH///oBzDepzMojVr1+/WnLGm2++WeUYMvgpaUbb73jPm28QjTa+uONJ5kOGYRQ+eQvn1IqmtEalkEVKZoXIzZw5E3BuHyWNjxo1CnDJGpmGymUyPq3UcklIImWSbK9SSr5kTiatT+RCMkv6TJ8+HXAJ+uDCWf060n6ShpIclFaZSn3tsObQN7iqppeSgKQiRN6j2i3or3YcSgGVgUx/9TntVH/++eeEuxWTzIZRYmTdAOYn0kcEMwBOL9TnHn/8ca677jrAOdylX2sXoeIEkuoqSpBPhg8fDrgwPkmrdPjxxx+B6gUORDISOUx0fTW2d955p8r7e+yxB+DKOk2cODEw3sXq7KD5lxtSuy6RrY4XNWvWDH5b95PmSpJYbqZ9990XgK5duwLuXm3YsGFwfpK4CgG99tprATd3KtzgGzllNAQnkf2ih6liktkwioS8d7SIluCerE54+eWXA1ULG6RKtooTqNSPAilOOukkoDKMVRZ9FbtTkYL58+cDcPXVVwOutFImbowwdWZZ6DVnsu7KrSh7werVq5OWrAph1S4rHTKdw8hQW3CljXUfal5UyHDNmjW0aNECgFmzZgGVhRrBBQ8pAEXS1k+0ibTWpxKuGg+TzIZRJOStC2Ss3jyQWCIrVE6F/saPHw/AcccdF+YppoQKCiigRf7lCy+8EHClgZYvXx6U0lHxOr88jYJjkkFBKdkouyuJq8AeSaYPP/wQqB5mqyL+2lkkg4ItZEvRb0beF2E3PfCPp78673vvvReAqVOnAq7opIrgz5s3L4gFOPjggwG3axEaj6zz8fRhvSe9Ot3xmmQ2jCIh7zpzMsgHqcLjsiBKX1GYXTpSKh9dIKVvyQIuCS0rtiLcVPQuE9LRmSUZ9PuKiJKUufvuuwGXzC97gEIbGzVqFNgD/GMKRcrJEq7wz0Qpq9FIdQ51Lr5E1P2jXZUszhqfxt2lS5dgNyLprZgIhX6qXNCMGTOA6m15ou1IY0lk05kNo8QoSMmsFUqpf4qmkgQ75JBDAOfvfPLJJwHYb7/9Uv6tTCWzfISK4hFKC5RVU4Xup0+fzu233w44qaRyrc8//zzgrNhh6MFhWLN1jzz33HOAizuWpJZEk5W7du3a1Qrcyb4hS77sHopuky9bUWSp+JkznUNJXO1ANGeywci/Lp/xihUrgnHIn65rosIa2mXJ/qP7QxbyWDEE0TDJbBglRsFIZukvd955Z7Bqn3DCCVE/G6trfbbLtEb7LWV0+SluQiuyLL+jR48OCvnJJ6nVWxFUqaR4JophTkcyq/mAmhH4qYGSwPK/zps3D4AzzjgDqNyt+FlFfpEGSS5J8LB86cmMT3Oh1FrFSWtcOjedq/4+9NBDAIwZMybQjSXNtfPSLkv6tnTlePaceM0E/PHFwySzYRQJeZPM0j9URlfSKlpjsVjnqNUunWypiGNnpG+1a9cOqB4tJIuoXpcFPlprT78tSpiEoTNLCkmX9KVuNOQ/PuaYYwC3g1HUmNq7qJxyLiWzj1q2apcgq7zmUFF8al375ZdfBp+56aabAFfIT5JX2WKSzNp9hdFiKBYmmQ2jSMi5ZPb1A+W+qsF4eXl50GSubdu2AEybNg1wuo5WOfkkM7H6ZrqqS9+S/qUoKKHm8srIWbx4cfBv+Tdj5bP6Ob7pEGZstkojHXXUUYCLCNMuS9L3pptu4qyzztLvRz1WmFFdmVYa0Tno3pRFWtddJZcVe960adNgh+Hbb2S7kDdD+rdI515NVjIXjAEscsumVDNt60S+e00lg8rGqAazbloZ9erVqxeEn8qVFna4YiS5LBsUOY5UigtkSq4Cf6TWrV69OphPpXj6dcPCxLbZhlFiFIxkjkc2V/lch3OWlZVVk8BhpAHGIt9dIHNBPkJy/XtS4Z0ffPBB6L9lktkwSoyClMx+b6Us/1ZWV/VoAQH5Gt/fv2lzmCLRAj5UbECGrliEYQ8xyWwYJUZBSuZckg99K5eYzrz2Y5LZMEqMlCSzYRiFi0lmwygS7GE2jCLBHmbDKBLsYTaMIsEeZsMoEuxhNowiwR5mwygS7GE2jCLBHmbDKBLsYTaMIuH/AVogqMzodwR9AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dcccdf518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1250, D: 0.2945, G:0.2799\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd76039b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1500, D: 0.2074, G:0.2169\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5e66b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1750, D: 0.3291, G:0.08715\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5bb39e8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2000, D: 0.2102, G:0.2133\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5abba58>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2250, D: 0.2323, G:0.17\n"
     ]
    },
    {
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SiUTOmmJRPMyqgf7pp5+m7T8ZnYupU6eG9qkSqpeufsylUK6HOflaKj1XZa5uv/12wHVw0T18xBFHlLxfM4AZRp1RkAEsm0QWkki33XYb4Co6ynilWtFrrLEGABdddBEA/fv3bxlQc3OwDJUUV88f9aPyixLIqKYlW/LSzV8hyL1VTNJ4JsK6F2o/6pOlbpcLLbQQ3377LZAu2TTufFdLcVf6VCSeKo2G7W/NNdcE4NJLLwVg1VVXDXpIderUKeNv1lprrUjHGgd+3zQZaz/99NO0FdmHH34IwNFHHw24DhblxCSzYdQIBenM+ZScUVkeGXfCdFX1tR02bBgAkydPxt+2JMEbb7wBQK9evTJuS++/+eab+RxGCtVgPMlQBifldYnbLlhnVkCPVgr+uHTttthiCyC8Rji4fmHrr79+yraiJK5rKKmrlZTu7Y8//jj4v0pcqbvjgAEDAHcO8+khngvTmQ2jzihIZ/YlcqYgEum7+mz77bcHXEf5kSNHAnDllVcCrsNFJmkvXbhr166A0z/VgUCuEPWtkntrbkHHLqqlUITvLvJXVxq3Cv5nkrZaccmq+9prrwHRrjriRnYVdfa45ZZbADjjjDN49dVXAbei3HPPPQGnV8sDI6KQ0LkwyWwYNUJBOvO8886bgHTpq5m8qakpbVbX7K2ZSxbp+eabD8jeBVJj69mzJ+Ak8c477ww4fWyxxRYDXJ8fjatNmzZpfYSWWmopwHUfrITO7AfO+EQptaLQmZO2lfI62zglzWU7kXVb7YmiXEVFfQ3V3fLdd98F4K233gJcieeBAwcG58JftUiX9o+/FExnNow6oyCdWVE7YSTP5JqJVch77bXXBqBz586A0yGkBydvWxZC+SjffvttAIYMGQLAHXfcATjpftlll6WMQ1bIZZddNq2RXRT9gEslzMI/c+bMMo8kM7lWDD169Mi5DR3jrFmzABfpp2iyambChAkAHH/88YCLGTj33HOBzOdH8QaSzMsttxwAkyZNAgovsFEMJpkNo0YoSDIXwssvvwy4yC6/2/xNN90EpEv7hoYGHnzwQcC1RZVOLOu2trXbbrsBcMUVVwBw8803A06nVwRPNTH//PMzaNCg0M/mBnwrfCZ+/PFHwEniqCLuyoFsGopo0zHIa5IJ6caSzC+++GLK66R2SDGMuAWTzIZRI5SUAik96LPPPgvek56rqJkwJF0z6Y+ygEvfkkVauotigV944QXAzfqSxIoL32effbIdi/ZfVmt2Q0MD7du3B9zx+WOKkiiypsKs2xn2BcCWW27J4MGDAdhss80AF6Mfh80iamu27rNrrrkGgEsuuSTl8+SViewzyj1Qpt8333xT6jACzJptGHVG5MUJZJ3WDByWNRMmhVZbbbUgFlvIUujnCCtzRUgXnTJlCtASbaa8Wl8KinL7mROJRJoFP85oqDiLE0gf1PiT7R/yVvh2C31XcfRh8faFEPU11P2m+2vEiBGAW+kdc8wxXHXVVYCT4rLzKEZbkY/KCiwlAiyW4gTNzc0JyL7ckqLvJ+uLbbfdFnAuKz+o4+WXXw6W0ePGjQPcUk0PgVIx1cd29OjRQEuFCyAlxfCnn34C3En364hV4mH2mVsfZrlf3nvvPe0r+JvvMUWdTFLM8WkMMnTpIe7WrRvghIP+duvWLVA91l13XQD+85//ADBx4kQATjnlFMAFOpWCLbMNo84oyHfjS2QtoZKT6sMkspD7SMkTH3zwAQDDhw8HSCk3o6B1bVMuKrms5JC/7777ABdconH16NEjbT9CS8RK4qsJcxuSyHJDScKNHDmSvfbaC3BLb5V8uvDCC1O2UQ2JFxrDJ598kjIWGbEUzKNUz6amJg4++GDArU5eeeUVwJW/kiFM7kaFhMaJSWbDqBFKiqrwy9y0bds20FGlmyp8U3z//ffBdwGWXnppAM4///y07UtKy/Wk/WgfkrqaMSWRjzzySAC+/PLLoGCcamxLn46yK0Y+ZHLBVcPqIApU3E6GySFDhgQGx8MOOwxw7q0xY8YATsdceeWVyzrWbEgi6z7SPapQ5GT7zt133w24VEfpylptadWi+zqKwn65MMlsGDVCpGWD2rRpQ+/evQEnAadNmwY46SrdVT2mJN0VSJGcMqb9KE1S25Q+okQLWbml28gqOXXq1DQnvl96t1zWbKXTrbTSSsF7Oq44Qx2rtdeU7BuSzKeddhrgkhkKIapr6NuAVl99dYCgEEEykuIHHHAA4NxXSuzZf//9AQIXlgpYFoNZsw2jzog8aESSTzqSpI6KwMnfKx+efMp6H5w+q4ACWQSV2K6A90UWWQRwyRxKhZTEW2SRRQJ9TrOsr8OXSzInn2edi4EDB8a1u+T9VqVkTvZJg5N0xVi147qGhaQr+s0OVE5IK7GwtNd8MMlsGHVGJJI52ww2duxYwElXWfVkzVRhv6FDhwItVmdFfClK7P333w8+gxYrNcAqq6wSNk6gxQeoVEzpzj5xS2aVoBk/fnzoOOOk2iSzbyeQxJKeWkzaajWUS9a1lH6t45TurJTIYjDJbBh1RiTZ+9mku+JZVchPCRiKXVXDLVn9mpub09rN6LVa28gyLN1a/loVINf3p0+fXrHIooMOOgiA66+/PuX9vffeO7Dc1iPy1fr3jGwsflptIXHepeI37Ctk1aq0XVmzZZWPIjY7X0wyG0aNUPb+zMpwUltWxepKp3377beDNDJZs2WlXnXVVQGnS8vvLF1avupkv63f/MsnLn0rrOUMOF96rjj2iMZRdp15ySWXDOKcwwi771Q+eYMNNsh7f+Vu6TpnzpzABqRoRHlndFxRRhqazmwYdUbZJXMxSOIqj1k6suJgFX/tJ/vfdNNNQWsRP49ZFDurhzVx15jCWr0CrLPOOoDL+sqFbAHyABRCnJI5rNFdY2NjYO94+OGHAejXrx+QXmAibBt+s4JsxCWZ/XJJaiU8//zzB6sqeWcUv63rHmXhPpPMhlFnFCWZNftE0XojH5QLe/nllwNOAis22y8e+NJLLwEtEjBXFE/cOrMvcTp37hw0CCgH5dSZdYydOnUKjYUXsnMo8i9XAchslEtnlu+4qamJFVZYAXDemmWXXTblu37zhVKIpWyQf6JK6YscJQqZ080k11U+lOtGkCoQVossLsrxMEsNUhKNUlMh/WFWbSzVzIqCuK+h3GWnn3460LK0LmcdcFtmG0adEakBrKGhIW0565vwS0ESwC+3E1UQe6VDHeOg2sI546DermEYJpkNo0aYK1xTPhqzn0yeD34QSb3N6tVyjEo8UKnaQqhUgYlCiLLbo0lmw6gzIi0blEln9sm3b1EmcoVmFrOPapzVo6RaJXOU1Ns1DMMks2HUCAVJZsMwqheTzIZRI9jDbBg1QkGVRmrduFDrxwe1f4y1fnzZMMlsGDWCPcyGUSNEUtDPMIxUFAGm1Eg1kosTk8yGUSOYZK4zDjnkEACuu+66Co+ktlH8xtdff122fZpkNowaoWJZU3feeScAu+++u7YNRFsILR8q6dZQm89MLUOjopyuqUwx8cpBV5kdNf1TccYoqGbXlMojPfDAAwD06dMHgN122w2Au+66K+c2ylI2KAp0UOrW+OOPPwafqeaSEivU01f9l1WKpxTKfSMstdRSbLXVVoDrBvn9998DriuCKonGUXO5mGPUuFTXS/j9oVR7raGhIejuGYZqf+VKmpkzZ05ah0WfaniY1WdavcJfeOEFIP0cdenSBXDL79VXX53XXnst67bNz2wYdUbsktmvkyyeffZZwPWPEttssw077LAD0NLFEWCLLbYAnKRStwS/ImIxVHJW17lXkbuHHnoo4/d0nJ9++ilAQcXkopDMqsKqqqySMuoHJWmT3BMql9okaSRVQ4RV88xGua/haqutxhtvvAE4NVE903RvqkOLKsj6aGXS3NwcWuVWPdN+++03k8yGUU9ELpnzNepIL9RMLN3is88+o2fPnoAr3KfZbebMmRm3VcxsLiohmaUnymD09NNPA7DpppsCbraXkbAU4jCATZ8+HXA2DdkwdC+1bt06TRcOqyPevXt3oLSgikpcw7DnRqvJsOPRtd5kk00A2GqrrRgzZgwQfv+azmwYdUbsOrMKiH/++ecAnHfeeQBsueWWABxzzDGA06GT8V0dMvPL4i1JfdtttwGu708hlLsbwqabbspjjz2W8pl6FmfrT1Uscbqm1F9JVuwll1wScLo9QI8ePQDnkvKRvu1byguhXNewkGdFUlarlqFDhwIwaNAgAI466iigpQfXv/71LyDVk+Pt1ySzYdQTsYdzyuIpvWDzzTcHYJFFFgHgq6++yntbmrnUjuaLL74AnIS+8sorATjyyCNLHXbkyO/qS2WIRyKXg1deeQVwvtVMgSCyA4RRikQuFx9//HHe31W8hK637lF18FRswV577QXA3XffHSqRC8Uks2HUCLHpzOrq17lzZ/0WgB9++AFo8dVBfv13tQ1tUz46+eEUEqdtSk/Jh7j1rXzOb7J/Nob9V7TSSK7jj+LY47qGWnHk0+xv1113BeC///2vxpTxe3656F9++SV4JhZbbLGMvzGd2TDqjNh0ZklT+YBl4VTAfSG6siSyP4vrfc2gsiDKT11Ia9e4qFQCSSVpaGgIGgaGISt33Bx00EEADB8+HCjsOqgPeKYoRq0Cn3nmGQAmTZoEuCjFtm3bZtymvDvyQ0tSR4FJZsOoEWKTzPKdSh+QRFZy/Pnnn1/wNhUtlGv2qwaJLJ5//vlKD6FsaBXS2NgYrJZ8ZLmdNm1aWcZ0ww03FPwbrSoVreXnFcycOTOw3CsGW4Tdk0I2oiglsjDJbBg1QuwRYIrwef/99wGCXN4JEyakfE8RYb1792bYsGEpn0nPkH9ZvlrpMl27dgXcKqAQ4rKE+rpy8nnWrKy/WsXEQTmt2TrmOXPmBFF7iuLzvxMlUV9DZaX50jNTVli+9OrVC4CxY8cCLkc8H/K1Zse2zNYya8qUKYBbfjz11FOAC99UEIke1AkTJvD6668DsMwyywAuKaNTp06AC+9s165dyutqwp8kL774YgAGDx4cuCXGjx8PwMYbb1zewcWElqeJRCLtIa5mJGAU4HLAAQcALkzYp1OnToHwGT16dMpnCnGVQUznYcCAAUBhD3Gh2DLbMGqEyCWzpM1GG20EuHC9O+64A2iRTOCWln5K3IUXXhiY/cPQbKek7jiMCVGRTY2pFYkslJifaQlajCEqLiSJla6r5AcFGykoKUwyd+7cOajppdWhrrNKWymxRGWxiknPLRSTzIZRI0RuAPPTFjVzSXdWmqK+d9999wFuZpszZ07wmWb4GTNmAM6YJqmutMJSjCrlCufUcV911VXBZ3GGcSbtP3YDmK6HwmuzfUe2kyiDaAq9hrq/ZK95/PHHARe22bt3byC9wIDG3K5du6AIo+wEq6++OuB06IsuugiAE044AXChmqUaabNhktkwaoTYXVMqQapyKtI17r//fgB23HHHlO//9ttvgSNeBe5U0C+5tBA4l1U1SWbpRrLE+4kkyedbKZtKVI+DckjmakomyXV8v/76a7A6kFRV4QytLHw7jgJcFILapUuXIBxZ35W012utRLQPhYYWg0lmw6gzKl4EX6jkbqbyQZox5W+WhJb+kezfLJRylZzxCxf+vT/A6ZEqYBgl5ZDMCy64IADfffdd8J4sxkrW94NIlIjx008/lbz/Yq9h3759Aaczq5CAAjxU9EL3le6/YcOGBf+/5pprAPjyyy+B9KQMlUn+4IMPCjwqh0lmw6gzqkYy58P1118PwDnnnAM4v6ZKsgwcOLDgbcYtmc8++2wATj/9dO0v+Gxut2YrmkmtVpKL5UtKa9XkezlUNkoSrRQKuYYNDQ1BB8xtt90WcNGKGrOSgjRW6b+KZ9hwww2D8j9LLbVUxv3cc889AEGxvmz4fbh8TDIbRp0xV0jmsKQFtTiRj096ij7/5JNPgvKvYcQtmX3p9eWXXwZSSVFxe+65Z9S7DYhSMisySvpi2L3TunXroICjir3HSaGSOblpGzg7jXT/ESNGAK6E8LXXXgvAwQcfDMBxxx3HOuusAxD83WWXXQDXMO7ee+8t7aCSMMlsGHXGXCFBUed/AAAPE0lEQVSZfTRm6Th6XUxGSrlbm8ycOTNoRasVx3PPPQfEE6sdh86sRneKGciE4grUYC2K9rthFHsNNUYVvZg4cSLg4sgVV61VxqhRo4AWyS2rta6hpLoiw6LEJLNh1BnVm26UhIqy3XzzzYCbDeV/zpVlVQ0o0m2++eZLizCq1uwp6YzSC/2Gd9l49NFHgfwl8uTJkwFXjDHOAoiSwOuvv37Gzw888EDAFes76aSTABg5ciTQsgL0x6c8gXwppdlh6DYj25JhGBWlKnVmSWLpLqraIJ3lkksuAdLjYYupOFKuskGVIkqdWeWZVPpGcfc+u+++e2DNleU7DN//XAyFXkOtFpSvrCKTWh3IdyyrtzL/kvZX9FgzketeyVdnJpFI5P0PSNTav3o6vqiPsaGhIfH3w5MAEqNGjUqMGjWqqq/hBRdcEPy/VatWiVatWqWNu2/fvom+ffsmZs2alZg1a1bwflNTU6Kpqani1zDsny2zDaNGqMpldjkpt2uq3ESxzO7fvz/gQhSrjVKvod8Xrdow15Rh1BlzpWT2XTslbssk81xONV5DFTwI6+xYCCaZDaPOmCslc5RU46weJeWQzFG4l0qh3q5hGCaZDaNGKEgyG4ZRvZhkNowawR5mw6gRCsqaqnXjgn98xWS2hMXZ6v2GhoaMbV4zkc/+C43rrbdrWAtUvKXr3Iisstn6Jufx8GR9Px8bRdhDnDwh6HN/e0oKiKNsrxEdKozgt8ApBVtmG0aNYH7mpCVM27ZtE+BKxuaTxihpLinqf1fNyZTo3759+5zlZcP2qxWD/mqcye+pgECm4/t725Ffw/bt2wMwe/bsqDedF3Evsyudzmp+ZsOoM0wyJ816jY2Nib/fK3p7ccziYRFW7dq145dffsn4G0nqP/74wwxgczkmmQ2jzjDJHLFkDkPF4A466KBAwkq/9a3XCyywAOAa4+UznrAVgbmmStou4ApH/v7770DLddLKxz/farpw1113AeFFAwscR16Suaof5nIYHpJP1N8lYQryK/tLYH/Muui6ET7//POgr7R48803AVhppZVSfvv+++8D0Lt3b8C5nX788ceU7yWToftHRR5mjSNT98tkVBBA3x89ejTguk3kQ6kPs98rXGOeMWNGyhgzoRpn6vahXtuXX355yrbEEkssAcCnn36a9/hsmW0YdUbFgkYUkKF6wwWuEGIZUy6J3KFDB37++eeU9yRxOnXqBBAYpLQtv6b34osvHiyvJa2ffPJJIL3a5Yorrgi01NqG7BJZ6LO1114767HEgXoua5zJ+CuGvfbaCyDopqj399hjDwDefvttAFZZZZVIx9irV69gJaQeUttssw3gltMdO3YE3HlXFc9+/foBLdVhF110UQAGDx4MuPtZknjQoEEZ96+iBXFgktkwaoTYdOZC45oLMfJESaEdBPM9X/vvvz8Aw4cPB9J1J3D6lrpBbrjhhoALvpAhTPWcBwwYkPK+dOpslFNnLuQaSh/t0qVLyufqub300ksDLefND4TJsN9YDGCzZs0CXFCMbBo//fQTG2ywAeC6PsYpcU1nNow6Izad2e+S58/a0g+lG2VCYY/dunUD4MorrwTg8MMPB5y007Yef/xxwFkMS8UPkWzTpk1KCCWkd71XXyaN2T/+v/76i48++giADz/8ECCwbqur4tZbbw04N4f6Oe++++4AXH311WljzZSEUS6yJXWoX5P6NM8zzzyAu0YzZ84EnO1Ex6xzlKu/dino/ll33XUB1/Xx+uuvB1y/LL2fzIMPPgjk7thRTkwyG0aNUHY/syyE2223HeCsmaKxsTGQYvvssw8At956KxCuk2mGla9PVuJ8SNZHOnbsmIAWnSjb/rIhy7Rme21Detcvv/zCGWecAbjjUnDIRRddBMARRxwBpHcWVD9q+T3nnXfeQK8rZ9DIzjvvDMCUKVMAePfddwH47LPPgJaVhqz7Ope+n1mrq6uuuirjPrQ60WorG1HpzPL9aqWUj40myrLPWfZhOrNh1BMViwALq8SRCekl0mFFjx49AJg6dSrg/JzSw/IcR85ZPZ9INPkm5cOUTijd9YsvvgBgmWWWCVYQ8kmrC+Fjjz0GOF06rKBBnz59gBbLr6S6tuVLwDgksxLrp0+fnvK+9NuxY8cG1yZpv4BbbY0YMQJwqxKdP+nf6vR5+umn5xxPVJJZ3R91/lU4QFbsZP1Y1/Xggw8G4MILLwScpyHMm1NM9RqTzIZRZ1QsAixMEidLIUkbxT/7ElISOSp9xZ81L7jgAgBOPPHEnL9V8QG1I5GE1LZOOukkoGV21wpC37nuuusAOPvsswGYNm1ayueypnfv3h2A9dZbD4CJEycGqxV9N6xIQRTIci8fsc773XffDTidc9y4ccF3tUrSyuSWW24BnBfj5ZdfBmChhRYCXBO3cuDfT1pxSM9/5513ACeR9f2uXbsG/1cPcf8eDJO8cXoZTDIbRo1QNVlTGsfYsWODUjuSNr7E9H2qpUjmbPqWVgQaW/KsqphrSR5FaT333HOAKxck/UvS6+STTw6O55xzzgGclNJK4NlnnwWcpFhjjTUA2GyzzQB45ZVXNN60OO+k40o7vkzHWAo6Dq00vv/+e6DlummFoMgo+d2XXXZZwJ0voXNdDMXqzMpCU8Td0UcfDTifuO6r7bffXvsBWo7PH69WiYozUFy9rtWoUaMA2GmnnfIdXoDpzIZRZ1RMZ27bti2QHj0k6QPpkvHYY48FXKK/rLpxkUm/UTbMjTfeCDhLp6TUWmutlfJbfW/MmDFAi74lnfO0005L2bYknHyv/uwvia0oqj///DOtkKCfR1wOxo4dm/K6qakpLYtIeqdWI5KG8847L+DiDZRNVQ5kk1Hsg/T1t956C4AtttgCcPHiIvm66Free++9ADz99NOAy0H3ibLVq0/VLLMzVXj0l9k9e/YE0pdopZBPcYJMrindALrQehAV2KFUSS2VNfa33norWIJ9/fXXAOy3336Ac8coyUDBGLrp5MaRcSmf4Jgol9kPP/wwALvssgvgJmLdmAoaSSQSwc3dv3//lG1omf3xxx8D0XSOLLVajIJf9CAeeuihQPp179u3L5AayKJ7xZ94n3jiCcAFx8iNJ7dkIQZKW2YbRp1RNZI5mbAxVToFsmPHjsEMrKR5lYcZNmwY4FxUW221FQALL7wwAFdccQXQMiO/8cYbACy//PJA+hJUdaNkZHvxxRcBF+apZfbkyZNDXR1J5XpiT4HU9dJK4ZFHHgk19Mh9FWUCRdzVORU8olVWnz59eOqpp4BwdUb3ia7tCSecAMDFF19c8P5NMhtGnVF1knnWrFl06NAh42fSU5VcEAX5zOrSd6XDggvGHz9+POAkjQrS6a8KCug8n3LKKTz//PPaH+BCGWUcOfPMM1P+fvDBB4BzkSiYodDjy3aM+eBXqdQKQbrzMcccA8Bll12W9lv9RsesbUVB3JJZdhAFtsyZMye4R/0yUhnGBrikGZUoOuWUUwBnMM2xDZPMhlFPVFwyy9muwPxC+jpFQSGzenNzc2BJlmSZMGEC4KSUXDIao0Iv5e5I/q5KuyqgRBJX1nqlGqrInVYBkuDJ56oSvaYUIKECBHI3ZrqGkm4qrDBkyJCohhG5ZA5zmxaDzoUs/gprLXAbJpkNo56omGSWDpHcJSCP/Ue1+4BiUyDlk1RJ26FDhwIujE8BLbKAJoei+qmNOhd+mV75KhXmqVVAMpLyfjmjTMeX7RiLQcX4tt12WwBuvvlmoCUQxE9DlUV+4403BqIttxOXzhwWRpzPMyObiqz38qfLz5yrOUAyJpkNo86oeBH8sH5LycRZkiUT2YrjaZzyIw8cOBBw0VnyFWvGVfK6UuWam5uDY19mmWWA9NI6mr0VTvjqq68CLSmPkCoZcpVSigONT4kVSgSRZJ49e3ZakwPZDuYGNGYdgyhkFavoPj9CLDlZI2pMMhtGjVA2nVkSWBZCFbyTFVbldsDNXrLqKpmhXDpzNsmstDkdh3yqisHVd19//XXAdQNUCuBxxx0XbFfRYn6TNJ0rBf6r1K7PjjvuGDQ9E36xgjlz5hStM+tYZdeQTn/uuecCcNZZZwGZJZZiAVQyV/p1165d89193kSlM2s188gjjwCuqGAxaKWmMkK6L2Qp1zlNJBI572vTmQ2jzohNMkuv8q2W8jf6pViVTfPtt98WVfSsWLJl3PjWy4aGhuC4xEYbbQQ4v6nalui3sjLrb1NTU5D2p/fks9ZxS+qrpK6KA8onn4yvG/vnrBhr9qWXXgrA7bffDjg7gPTeXr16Ac7frdfS7cGlNkYZrRdGXH5mRd7JR6xzO378+OC6K8tPHgfFE2jFpBWazlExRRhMMhtGnRG7ziw9S7OdkE4hKaQWmX/vp6B9lFLELnnWa9u2bQKcxMykOyunVcUGJKlV2F5tP/1WpPkck3RQ/S3G4hlFs3VJoFyx8CqRdNhhhwGprYbU1E5+5a+++ipl21EStWTWOfTLCin+fty4cYGEVe65CLPzyDOh+6YQTDIbRp0Rm2T2JVFYQXd9z2++XS4KndW1ClCmkyzOkqaqsHHPPfcATu9SkbjZs2cHVUlkzZZFXBUschXxz2SPUN60JGDSaqJoa7akqP6uueaagNMDtYKRzz1Xjm9cxJ015bP88ssH9gsVAVReu65JXFlh2ah4ooX2ry72F110UeDOKAeF3AgdOnRIS3nzHyylximMU4EVeqg7d+4cBCNEEcgfNmlG8TDrOqjTg2+Y1JJSIazJFFMep1jK9TDrGvtG0LixZbZh1BmxS2YVqTvuuONS3vf3K0OZCvuVi3yKwSVLJBnytMRUKKPqI5eDZCNXWPB/FJJZSBL7y2cZgWQoUhEHJVWUi3Ivs8uNSWbDqDMqpjMrrM/vW1RMT+RSSJ71mpubE5DdJRRFQIvvSvNf65zoHMlF5BvGGhsb08YxadIkwKVmxpkCKckcR9JAIVSjZFa4cjGrFLn45N40yWwYdUbFrdmVppBZPZMkTPqttpdzn5Luvrsul/6badu59hunZK4WqlEyR4lJZsOoMwqSzIZhVC8mmQ2jRrCH2TBqBHuYDaNGsIfZMGoEe5gNo0awh9kwagR7mA2jRrCH2TBqBHuYDaNGsIfZMGqE/wdTiC7NVcoATwAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd53a0b38>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2500, D: 0.201, G:0.2389\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd54c0710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 2750, D: 0.2284, G:0.1609\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd55e6588>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3000, D: 0.2229, G:0.2093\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd536a518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3250, D: 0.2227, G:0.1536\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd53dd4a8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3500, D: 0.2303, G:0.1605\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd56bdf28>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 3750, D: 0.2324, G:0.1899\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5cd3470>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "D_LS = discriminator().type(dtype)\n",
    "G_LS = generator().type(dtype)\n",
    "\n",
    "D_LS_solver = get_optimizer(D_LS)\n",
    "G_LS_solver = get_optimizer(G_LS)\n",
    "\n",
    "run_a_gan(D_LS, G_LS, D_LS_solver, G_LS_solver, ls_discriminator_loss, ls_generator_loss)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Deeply Convolutional GANs\n",
    "In the first part of the notebook, we implemented an almost direct copy of the original GAN network from Ian Goodfellow. However, this network architecture allows no real spatial reasoning. It is unable to reason about things like \"sharp edges\" in general because it lacks any convolutional layers. Thus, in this section, we will implement some of the ideas from [DCGAN](https://arxiv.org/abs/1511.06434), where we use convolutional networks \n",
    "\n",
    "#### Discriminator\n",
    "We will use a discriminator inspired by the TensorFlow MNIST classification tutorial, which is able to get above 99% accuracy on the MNIST dataset fairly quickly. \n",
    "* Reshape into image tensor (Use Unflatten!)\n",
    "* Conv2D: 32 Filters, 5x5, Stride 1\n",
    "* Leaky ReLU(alpha=0.01)\n",
    "* Max Pool 2x2, Stride 2\n",
    "* Conv2D: 64 Filters, 5x5, Stride 1\n",
    "* Leaky ReLU(alpha=0.01)\n",
    "* Max Pool 2x2, Stride 2\n",
    "* Flatten\n",
    "* Fully Connected with output size 4 x 4 x 64\n",
    "* Leaky ReLU(alpha=0.01)\n",
    "* Fully Connected with output size 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([128, 1])\n"
     ]
    }
   ],
   "source": [
    "def build_dc_classifier():\n",
    "    \"\"\"\n",
    "    Build and return a PyTorch model for the DCGAN discriminator implementing\n",
    "    the architecture above.\n",
    "    \"\"\"\n",
    "    return nn.Sequential(\n",
    "        ###########################\n",
    "        ######### TO DO ###########\n",
    "        ###########################\n",
    "        Unflatten(batch_size, 1, 28, 28),\n",
    "        nn.Conv2d(in_channels=1, out_channels=32, kernel_size=5, stride=1),\n",
    "        nn.LeakyReLU(negative_slope=0.01),\n",
    "        nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "        nn.Conv2d(in_channels=32, out_channels=64, kernel_size=5, stride=1),\n",
    "        nn.LeakyReLU(negative_slope=0.01),\n",
    "        nn.MaxPool2d(kernel_size=2, stride=2),\n",
    "        Flatten(),\n",
    "        nn.Linear(in_features=4*4*64, out_features=4*4*64),\n",
    "        nn.LeakyReLU(negative_slope=0.01),\n",
    "        nn.Linear(in_features=4*4*64, out_features=1)\n",
    "    )\n",
    "\n",
    "data = next(enumerate(loader_train))[-1][0].type(dtype)\n",
    "b = build_dc_classifier().type(dtype)\n",
    "out = b(data)\n",
    "print(out.size())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the number of parameters in your classifier as a sanity check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct number of parameters in generator.\n"
     ]
    }
   ],
   "source": [
    "def test_dc_classifer(true_count=1102721):\n",
    "    model = build_dc_classifier()\n",
    "    cur_count = count_params(model)\n",
    "    if cur_count != true_count:\n",
    "        print('Incorrect number of parameters in generator. Check your achitecture.')\n",
    "    else:\n",
    "        print('Correct number of parameters in generator.')\n",
    "\n",
    "test_dc_classifer()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Generator\n",
    "For the generator, we will copy the architecture exactly from the [InfoGAN paper](https://arxiv.org/pdf/1606.03657.pdf). See Appendix C.1 MNIST. See the documentation for [tf.nn.conv2d_transpose](https://www.tensorflow.org/api_docs/python/tf/nn/conv2d_transpose). We are always \"training\" in GAN mode. \n",
    "* Fully connected with output size 1024\n",
    "* `ReLU`\n",
    "* BatchNorm\n",
    "* Fully connected with output size 7 x 7 x 128 \n",
    "* ReLU\n",
    "* BatchNorm\n",
    "* Reshape into Image Tensor of shape 7, 7, 128\n",
    "* Conv2D^T (Transpose): 64 filters of 4x4, stride 2, 'same' padding\n",
    "* `ReLU`\n",
    "* BatchNorm\n",
    "* Conv2D^T (Transpose): 1 filter of 4x4, stride 2, 'same' padding\n",
    "* `TanH`\n",
    "* Should have a 28x28x1 image, reshape back into 784 vector"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([128, 784])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def build_dc_generator(noise_dim=NOISE_DIM):\n",
    "    \"\"\"\n",
    "    Build and return a PyTorch model implementing the DCGAN generator using\n",
    "    the architecture described above.\n",
    "    \"\"\"\n",
    "    return nn.Sequential(\n",
    "        ###########################\n",
    "        ######### TO DO ###########\n",
    "        ###########################\n",
    "        nn.Linear(in_features=noise_dim, out_features=1024),\n",
    "        nn.ReLU(),\n",
    "        nn.BatchNorm1d(num_features=1024),\n",
    "        nn.Linear(in_features=1024, out_features=7*7*128),\n",
    "        nn.ReLU(),\n",
    "        nn.BatchNorm1d(num_features=7*7*128),\n",
    "        Unflatten(batch_size, 128, 7, 7),\n",
    "        nn.ConvTranspose2d(in_channels=128, out_channels=64, kernel_size=4, stride=2, padding=1),\n",
    "        nn.ReLU(),\n",
    "        nn.BatchNorm2d(num_features=64),\n",
    "        nn.ConvTranspose2d(in_channels=64, out_channels=1, kernel_size=4, stride=2, padding=1),\n",
    "        nn.Tanh(),\n",
    "        Flatten(),\n",
    "    )\n",
    "\n",
    "test_g_gan = build_dc_generator().type(dtype)\n",
    "test_g_gan.apply(initialize_weights)\n",
    "\n",
    "fake_seed = torch.randn(batch_size, NOISE_DIM).type(dtype)\n",
    "fake_images = test_g_gan.forward(fake_seed)\n",
    "fake_images.size()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Check the number of parameters in your generator as a sanity check:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Correct number of parameters in generator.\n"
     ]
    }
   ],
   "source": [
    "def test_dc_generator(true_count=6580801):\n",
    "    model = build_dc_generator(4)\n",
    "    cur_count = count_params(model)\n",
    "    if cur_count != true_count:\n",
    "        print('Incorrect number of parameters in generator. Check your achitecture.')\n",
    "    else:\n",
    "        print('Correct number of parameters in generator.')\n",
    "\n",
    "test_dc_generator()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iter: 0, D: 1.435, G:0.4759\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5e69828>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 250, D: 1.256, G:0.7359\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd56a9d68>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 500, D: 1.263, G:1.328\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd74abef0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 750, D: 1.272, G:1.311\n"
     ]
    },
    {
     "data": {
      "image/png": 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cZ84kGjVqVG1+XrI51rzfyeTESKVSjgPHW/srCLwOMHfLWjDhN+84040hVwdMtsqnme6JN5XS+/1s6DtygLnnmGl+6Y6f77PqduKF2TFFuePl5eXBt3RNenZNEubnXUQqKiqqPQjeOebiVY379cjW0jVuTRD8kC6Ong2rZlssCaGgskF1edWLGj/x5SDOkymeK/T/UuXcMfpMUjtuTfiykclESKVSdWL8+WAls8WSEAoqTpC0Fa4uzieTdlTbXPT/cvalc0jV5uSLM36y0uKK3+tsJbPFkhBiV2o3F7we27qwy0k7wEaMGMGWW24JmFaohYw/0+qd6+fpJHmmUFMY1zlT3nVQ5wrbSx8nj3/stkBmQy/EQw89BEC3bt0AU5M5H7KFNYJEXTo+//xzp2LniBEjAHj66acDP1+msEZtc3S/yN4XK58H9g9/+AMAkyZNAqBXr16A6Yr4/vvvA6Yi5ty5cwFYunRpzueI6h660XXaZpttAFNq6KOPPgKCLY9kt0BaLPWMOqVma6tjz549ASPt6gIa+0YbbVSjP1EYktkv6RxFhUhkSSp16VDnS51niy22AEz/YtXTVpdIP5K5GKhjifpMq8a26oDffPPNgOlgqgKAYWIls8WSEIommbVCN2/eHMht5dLfyA5Ltzk9rkjKrV692ikKqEKFYZAtaQL8O8780KhRI0aOHAkYG1ndSHQd1KVEv6uu9i+//FLw+aNA5YWlYXi1LfXY2n///QF46623Qh+TlcwWS0IoWq+pLl26AKYbwtixY4GqlTnT8dVpT15tdRGoC2jl/vrrr50C8SqqHnQ4JhthSmRpSqNHj3b6LmtO//3vfwHT8WHAgAGA6cWl52H48OEAzJ49G6i+gy2fMkVh0LhxY6c/mO6r7qEK5qv0VSG2f20puTW+n/eZLBZLrIis15SkqcroamVTFz11EJTtlA6t5toHrFYxdQGVCV6xYgXbbrstYPr+anUPsudUpnsVZpKD7uHAgQOdzpCnnXYaYLzV8vrOmjULgI4dOwKmmcCCBQsAqvUbyzTmYiVq9OnTh1atWgE1i0muWrUKMJ0xC4k324J+Fks9JXRvtqTOoYceCsARRxwBmOytCy64AKiyJTMh20F2tZq06WddQNpEp06dnBVXkllSKUhqW9XDkNBnnnkmUCVVTz75ZMDYyDqfcgNkK8sm1v3/4osvahy3kHh3kCgK8de//tXRNFevXg2YeckfosYNhUhmu9HCYqmnhC6ZW7ZsCcC5554LGBtZmUEvvPACkN2Tu8MOOwDQoUOHascq9krtB81v3bp1jpTq3LlzaOfLdQtkEMi+PfLII4EqKdWuXTsAli9fDpi48sUXXwwYTUU+kiVLlgDm+YjjJhq1Fu7ataujWUgSq8iDIi7KNZdNnQ9+524ls8WSEEKTzLKVn332WQC6d+8OwHfffQfAlClTAOPlzHaMO+64AzCr+OOPP17w+KKOUUoSzpgxw/EXaDUPYyxRai2SzMrmKy0t5dZbbwXMNs+//e1vAI4nX/an4s9Tp04FzPMSpGe/UDS/fv36OZ9JImseQnHlmTNnArW3Fiqk5ZEXK5ktloQQmmSWzaQcVnn1ZCNvttlmgLEt0qG/1bFOOukkwOT5ZiqZE6cN48Kdi+7NS5dXO4qdNWEgz660LcVg3Z9pF5XuibzWBx98MAAff/xxtf+PE7pfH3zwAVClGZ5zzjmAmbvGLYnsLbag36WN6XN3JKPQuQf+MmvicgT885//BGDixImAcQgMGzYMgF122QUwL/unn37qJB9ceOGFgHGiKeFk6623BkxSvlLnpNLo+z/++GNsKkrq/HLmgXH6hPESR1G/S+cYPHgwYFTolStXOttUtWAJhRN79+4NZE8SKjbeRVfP1eDBg2u8jDILZALq/2Uq6nuZOoxmO3+uWDXbYkkIgZcN0kq03377AaYsjBwDCi999tlnQLDbGLXqKaXusMMO4+WXXwZMKCRNSmCtJWeCVNsXL15M+/btARO2kRkRJPmWDfKDtC0liEjDKCsrc6SY1FBJJmlVcnwFQdBlg3TtBg0aBMDtt98OGNOwYcOGzrMgrXCvvfYCqjTL38ZU7Vj5PDu2o4XFUk8JXDIrmK7V7IcffgDMJvWddtoJyCyR169f7zgJCuWVV17hkEMOAarsOEjbMC3SYnDff/+94yDSmCTFwsBbDC6IOcqZ9e233wJG+j722GMAjB8/3ilWqCSR77//HsCpTCpNKQiClszSHidPngxA//79ATNPMD6egQMHAvDmm28WetoaWMlssdRTApfMkrhasZ588kmgZoKEwkvjx48HcJIMmjZt6ti5bu8omAQTpUHqmN7AvWznBQsWsPfeewNGMmSzmf10SPSLjvXzzz/XCNPomoXhcQ9DMqtMrjzW8lCrVM6yZcucrY0ql7Nw4ULAlNXJ1DY3H4KWzEo9lRYxdOhQwERe9t57b8d7LR+AnucgsZLZYqmnBB5nVqxXXuw5c+YAcOKJJwLGi5nJu7xmzRrH3paHUGVLX3vttbTn1Ap2xhlnAHD66acDVdvqtIL6aeKd6fcgcNuKda0PklIYt9tuO8BEDSSR5dlNpVL06NEDMPkG+l3x5TBszKCQlP3yyy8BuOeeewCjibRq1crZ+hiUfycIrGS2WBJC4MuKpJk2Z8t2zpVWrVo5HlDZypLytZ1Tyfp/+ctfgKpssyBXzkJ6++rvVq5c6WS4uY/r/k4QhCH1FRlQdpMkswoQuFGxRaVtqmCfohpxlsyanyIzyjzU2FOplJMj4LfoXpjEZyQWi6Ug4qPw/8Zhhx3mxPPmz58P5J67rLinigUuXLgwaK9pwcdYvny5E8cUURb0KwRl7cnuV3xcdrHboyuJpZK5W221FWDuZRw3wwiNSaWAtBlEGtXatWudubpjz2GNI1esZLZYEkLsJPN5553n2CraYeR3FVfL1/Xr14dSLK8QFixYwK677lrtM8WdC5HMUXjGFVNVxEI/JblUWGD16tVO+Z8+ffoApuC/bM04S2bNS3kN2rarHX/HHnuss3VXfoM4YCWzxZIQYiOZtVKXlJQ49pYaxOW6iut7spMrKioyFkWLQ4zXW9QuH2prEBckklCK92+66aaA8faqrM4mm2zilN3dfPPNq/3N22+/DcSrUJ8XPWfaL6+oijSojTfe2NkdVUjBvqCxktliSQi+crO9ucthMHfuXCeep5Wxtqbq3iJpytV226DeMUv6V1RURLpratSoUU5GkVqYaO93PmTSWsLYz6xjquTsJZdcAuD4AHSfGjVq5JRCko180EEHATBv3jyNK99h1CDo3GyhHG1lgklTBDMP5WuHKaG9+fWZKKjXVBhOjEWLFjmVPFU/yi9yesXRuXL//fc7FUqff/75wI4bhUPJW79LZZ2OPvpooHrlzTZt2gBwwAEHAJk3utQFlLqpF7aystIxOeLkYLVqtsWSEALfAlkozZo1c9RsrebqDJhmPNV+92kyANGr2UFTW2/nMLZAxo2w1GzXMXUe5/coNYxc1WwrmS2WhBA7yQxmW5m22qlDoEIECj15V8ykS+Z03RBq68lkJXPdx0pmi6We4UsyN2zYsBJMsoO3Wn9Q4QZJG4UGVIRcSe1KoVOyu0JQ2fr4eqWaq4i5s+qVlpZW5nqcTKGu2rSEkpIS5/i59lD2nlPnSqVSTskhaSveY69fv77aSTTHIArqeecssl23TGR7DjP9rc7vnqN3fnFOG/WSzjYHWzbIYql3+JLMFoslvljJbLEkBPsyWywJwVc6p5wLwuts2bBhQ63dBzM5ouTIKS0trdEpz2sKeB1UqtShihf6vKyszPl3pu57bre/18GXS1JKbW1l0/1tUKaN2wEmdG30+bp166oNRHP0hveCHluu1OagcidoeL/rcv7Veg/dzrpsDs6g8eOA894LhWi99zDj3+ez0ULExd7WRdDk3Q+018vrJVsR/CDH5jpfYMeGmhlg3vN5PaFRbJaJmqTGma0322KppxRUnMCrMqfLWQ1zE7pXImvbnbextfu7fo4rCmnHGWac0z3OTDHK+kYu1yFJWokbK5ktloSQl2TOtLIF5eTxrqZydOhz/T5kyBDAbBSXA+zzzz93vu+1JfNpU1MI+eSN+z12fSUX7SOXzLIorqOeTTnmctkH7Ve7spLZYkkIBVUaqe1zP0jabrrpptx9990A7LzzzoBpWOa1Q7XKqWHZ3//+d8BUwnDbzGFLZK/WoGMF2VQ8HX5t5CiqxURFtjnnMq8o56xSVu3atQNgyZIlQFUVk0zj8LYqro2iV+dUaOWCCy4AYMyYMU7NZW8M1Yu61z/yyCOA6RucKaYcBhq/xqzNIaq4+dNPP0U2FjfZ4rbu/w97XLo+rVq1AkzpHdV3K8RBGpcXNRvaHPTee+8BplqpntWBAwc6ZYm8+O3GYtVsiyUhFF0yH3/88QCMHTsWqNre+MknnwDw2GOPAaY+c+fOnQGjqqjbo7pE+unFHFToRibAAw88AJhexSrad9FFF1X7/ksvveRI6yDJVRJFJbF0fdVjSsX/Pv74YwAmTZoEmIIT+YwrrLlImxgwYABgylap0qgf1DO8U6dOgJG29913H2C0y3RYyWyx1FOKVjdbHQTVGUAJH++99x7XXXcdADNnzgSMXSWpN2zYMADuvfdeIHPBv2ykS5Xzkwqovx81ahQAd955J2ASWLw54fr+6tWrHfvp7LPPBsw1UNGFTLnI+VCsskEqtTt9+nTAlIDS9VD/5jFjxgBGQudD0Cm5yvVXF1J17JBfxI+dL8mr0NTQoUMBeO6553I+hi0bZLHUMwIJTflB0ubiiy8GjM2p3r9jx451Ogiol6+82vpdZYT69+8PmCQRP2GgQueiv5eX1usdlgTST9lhTZs2ZY899gDgzTffBMw1+eGHHwATklAXQknwp59+GshNMhQjnVPn7N69O+PGjQOMf0NJEtJC5O1393SOC7Jvt9xySwCeeOIJwJ9E7tmzJ2C82flIZL9YyWyxJARfkjmIDQiyR7T6KcZ24oknAvDOO+84Heplo0giqyeT7K+PPvoo73EExeTJk6uNYfDgwQBOPynNT57QE044gT333BMwElj2peYtTUQayOmnnw6YLoyZ4pJuopTM0jq6desGwJFHHuloXM888wxgOkQqziyJHKf2LrruZ511FmA0vXPOOcf3saR16bkIUyILK5ktloQQuTdbNqaapkk67bvvvkCVFH7qqacA+PHHHwG49NJLAZw0T0kAxXLVlTCf1MmgNrbLi62finnLZpL0at++veO1lk181VVXATB8+HDAdFNUex5J5ieffBLIbZ5hdIHMhHfurVu3djomKiX3H//4B2AaGhx55JEAvPXWWwWfP6h7qGfw4YcfBkz3x969ewO52czSSJRLoEiLntl8sN5si6WeEbk3u0OHDoDJ6pIHV5+fcsop7LXXXoCxp+T5lCT79ttvAROrlhTIJ0MnKLzea+H11n722Wc1VnhpHuphLCn+wQcfADBt2jQg/E0bfnEV2q/2c8WKFWy00UaA2fwiDUXebGXtxQHZyldccQVgtKpBgwYB/rzYXvu6kN7bfrGS2WJJCJHnZl9++eWAyYi56667AFi6dClQlfWlmPPjjz8OGNtZ9vYxxxwDVElxgNtuuw0wki3OpFvllf2mTCNJZtnS8gD7IQoPf6ZzbNiwwckB0C4haVmPPvooYKRfMfHmj7dv3x4wPgr5LPwc69xzz632+VdffVXoMHPGSmaLJSFEJpnloe3bty9gVmZlb0n63HHHHRmPIZv4hhtuAODUU08FYPfddwfq7kb7k08+GTC225w5cwCYNWtW0cZUCE2aNKlxT+TBV9w5Dkg7lDSVzf/hhx8CJidC9r7yrNP5Lo477jjAePS1ryBKInuZd9xxR8ColF51M5dECCG3vxYAXfS69hJ37doVgDPPPBMwD5PCNnFzeOVKs2bNnLlpDu+88w5gnJlxQE5YLTx6EfWsKuSpxVbJPbfeeqsTJuzSpQsAEydOBIxqrpc7SqyabbEkhNAls9Ss/fffv9rnCjOpsqbUsFzQaii1VOGOuoK0Ezn4JBHmzp0LmMSKuobudfv27dliiy0Ao5pqW2sM72CBAAAO6klEQVSctI1ly5YBcNlllwEmpVibgBReUzqxTMPjjjvO2TihCrG6h+effz5gtEal5ErzDFN7tJLZYkkIeW20yFQR0l2n2vs3sqG0ymmDwooVK3yfX4F4bR4/6qijcp9EkUmlUmyzzTaASf3TKn7llVcC/svFxI1evXo5Dk+FZl588cUijig9uu4q+aQyVbo/su9bt24NmNBoRUUF5513HmD8NQq9zZ49GzA+IT+lrArFSmaLJSH4ksy1dRxMh1YkSWalN06ZMqXa/+eCbGQlmGgccdpGVxuNGzdm9OjRgFnxZUfKZq6raNPM0KFDnXsjiRwnWzkTkqJKoxXexI8mTZo4iUyap7Y8qma7NNAotSwrmS2WhOBLMntX11wKz+n/VLBNdqJWNj9IM5A3W1K+kGJwUdO2bVsOPPBAwNhb6sjhx38QJ3RfVA6pb9++ztY/lRoOsxto1LRo0cLZ5KN7pw4Vku7F0BatZLZYEkIgceZcOu3J86ztiiqWtnDhwlqPr2McfPDBQFXpHYAZM2YAptVJnNEcOnTo4FwDSTTNo655sTWnFi1aACaTraKiwokwxCnjKyg6dOhA9+7dAePHUUmnfAoUBpWGbCWzxZIQQs8Ak62kxHPFiLVtUTaVvIHr16+vseLfdNNNgJHMspGVIF8XvNma04ABAxyvryTx9ddfX7RxFYI0C2lZ8s7Pnj3b8erWtXz5bGi+N954o+P7ef311wGYOnUqkJ9vIKhrZCWzxZIQQpfMWnVUSPzwww8HTJkgbVaXlPr666+dVU+7WvS77C/ZzIo31wVkW3Xo0MG5JvLoqxhDXUPaxj777AOYDKnjjz++ztn/uaBc7Z49ezq52JLMcZivlcwWS0KIbD+z9iDL7lURO+1U0U6iNm3a1Cjkt3jxYgBGjhwJmF1S+bRuzZRXHjaK0Xfq1MkZg1b1OLZoyQVJKuUpqyGeIhdJQ40L5fOAeBUmtJLZYkkIvorgB1lAXaubiqltttlmQNW+ZnkEFy1aBBjvtT4PIpuo0JauflHrmaVLl9KsWTPAlFB64403wjptqC1dJ0yYAJj4snwbUUcXgiqCXxtqkH7EEUc4mpaeWz/F//ySaxH8yKtzCm0/+/jjj6v9DJq4qNmqN6V6UgCffPJJpGMICl07qZ3aTBH1Sxz1PVSl0VQq5bzMcUpYsmq2xZIQCipOUBfIVts5SlQ+xy2Ng5BkQd+TVCpV67EUglLBu2HDhgVy7rijrqN9+/Z1tjiq68X06dMDP59fzcNKZoslIRTNZg6SfPpGR21vqaDb4MGDnVVd0jpXohhzLptmZC+qg6O6cSYd1Wk/88wzndRVJUEptOr3nmbDSmaLpZ7iKzTVqFGjSqhZpMBtt2XyFutz7/97SxFlHexvf+P9qU3+XsrLy53jeuep9Mry8nJnoI0bN67U36X7m3QrZaYCDd4xqsBdZWWlI5l1HTPN3Xs+XSv375nm5+rQWO0gpaWlle5zprO5M91DXWd9LmmkhCCfYc6M588VXQ/3HBs0aFBtfrmMwUsuY1E6p0oKqzOp0jp1b3WsbKW2vNfCe46ysjLbn9liqU/4kswWiyW+WMlssSQE+zJbLAnBb93sSsjsIMlHZa/NyZPtuK786ozHzzTWXJwnuTjn9J1MDr5sBGHiZHIK6qfbwQfGiZlp/21lZWWtnUtqG7d2U4HpIRaGOScnZr4OsExkelZU+eZ3v/ud4+BSKEqhR69z2Ovk9BNukgNs3bp1Of1R0TZaxIWokvSjQg9eJm+2d0EOawzexS0Mot4sI0pLS+ncuTNgFhQVpswUCcmHdPPLhlWzLZaEkIgMsKDwE/OMS556JrWtNnUu7PEX+7qESdOmTZ1tn2pNrAKVascbpGTOFSuZLZaEYCVzxLiztsIgV1s1TMlZzFY0YWocyoCbOnUqPXv2BOCCCy4ATGuhYs7dSmaLJSHERjLLK1hSUuJU41BpIW94I1Pea5T4Xfm1qu+xxx689tprQDCF/PxWTonClm3cuDEPPfQQYAo2hrGzKt1cwpzfKaecAlS1rFWb13vuuSe08/o9ZuQvsx46vbCbbLIJAOPGjQOgXbt29O7dGzAvwOeffw6YvrnqjjFnzhzAdOIrlCge9Iceesgps6Ptc0HgHXsx+yEfcsghThVW1T7be++9izaeoNhpp52AKoGjeu9hPjN+j23VbIslIUQumZXUoG4Vl112GYDTsziVSjnF7/Tddu3aAbDrrrsC0KtXL8DU01ZlxGJKo9rQdsHWrVvz5z//GYCTTjoJSF6nRHWAhKo64Ulhzz33dP69fPny0M9nQ1MWSz0lcsks6bls2TIA5s2bB8CQIUOAKjtZ3edlMyv/VZ8vWLAAMPZ2McMBubLbbrsB1e0g2cyTJ08uypjComvXrs6/5dhMAnrewHQjiRNWMlssCaFooSklpN95552AsUd69Ojh2ArqZCGbWOV2VIx86623BkwR9jghiTR69GgArr76aqAqzCZP/mmnnQbAlClTgGCLwRWTqHoxpdthFybueXXs2BEwRQ296P5vs802gPGZfPjhh4BpAlFZWel8N1MJqFyxktliSQhFTxqR9FXP5WOOOcbZTqbVSyuXJJn6+6i3UZzQmFQYfsyYMQB8+eWXQJU00fhVRP7oo48GTLJ+XUX3yW0nq+Cd9uYG0cdYmltU9rg0APfe9dpsZvmGcmm7FFQUxkpmiyUhFF0yCyWq33DDDY7toJV34MCBgOkYqVVeGWFx4tVXXwWMLaXYuOa0yy67OCmAqlwxa9aswMcRdZF/9zmbN2/ufKZi8bI3g5DMIirJrHsoW/bnn39myZIloZ/XxpktlnpKbCSzKC8vd1ZcreZaGVVIXgXH58+fX4QRpkdjlpfzjDPOAExsXMybN8+JVyrTTXalGpPVVZQX4PbG6p6pLphqZRWCqyRSxv8LsjjAvffeW+2Y48ePL/jYuZzXL1YyWywJIXaSubKy0onDdunSBajacgYmNj1t2jQgXg3L5JFUzq4kklZZ2flDhw6t4YXv27cvAC+88EIkYw0L+QVWrVrlSGLlnStqEeR5wt4CqXv39ttvA0aTmjFjRmDnSEe+xRCtZLZYEkIsJbNsr5NPPhkwWTTazP/ggw8CwTQrD5r+/fsDODujRo0aBcA555wDVHl6vV7YHj16BHb+YnixheKwpaWljlT54YcfAOP/UO5AEIS9/1wawBVXXAHAtddeC8CECROYMGECYCIqcdgfELuXuUGDBs4LocQLvdwvv/wyYFIE43ABvSj19OabbwbMy/XUU08BMHbsWKeyY7NmzYBwkvajTnUEY2o0a9ashpMqiKoqxUKO1quuugqAJ598kv322w+AiRMnAqYWWDGrklo122JJCEWTzFq5W7ZsCUD79u0B6NOnj7NpXyVnpE5LugWpqoWNO9EAqlZwSSut5p9++mng58uUIhhm9Uqd021GKGkkqsIRYcxPx3rppZcAmD59ulMWSSFIOcWUNFQMrGS2WBJCZJJZK6ZWNNmU3jDNmjVrnO1iXrtLySJx6SaRLwpFSYKp+mgURFEv2+2EU/GJqO5VFAX2xo0b59jMClfJnu7Xrx9QnBJWVjJbLAkhdMmsVVrFB1TAT9JWnsIvvvgCqEq26NatG1DT26skEq1+Qv8fx1BVOlSgUKjYQl1H99rtSZ80aVKxhhM4mt+6deucHlPy9Wg7a4cOHQCz5TVK7dFKZoslIYQumWUj9+nTBzBxOUlTFfZTwbtRo0Y5qZBKOFD3ANmWhx56KGDKuCq9c/r06YCxrcFfKZZMtrjbBgxipfWmNkoDCZJiJI9IIru92WF6d9PNsRB/itKIpeFlymNo3ry5s1lGabpKigmyP7NfrGS2WBJCaJJZdq281moxoz5Lkqoqq6Pvyy4GeO655wC4++67AVi0aBFgPOC//PILYOzvoBLvvat7OslciASYPXs2YOY8YsQIAG655ZZAxxo1kmRuT24QWx7DRkUitLVROQFTp04FTM9lSeFmzZo5G0l0vR9++GEgmuL4mbCS2WJJCCk/kiWVStX6ZdlLylGW/avzyO515/GC2cRfUlLi5F5LYi1durTaMXyMN5c+xY4o0/xykcyFoGILanyn+e64446+j1WbhuCeH0BJSUnWCRQyP42lvLzcOY7s0JB7Ute4h/mgslTSnPQsK9Ly7rvvAlVF/hWd0XOswv/SHl1jy3c47mPkpG5ZyWyxJARfNnMudqLKyCozRnhXZtlSKjBw0003Oee46667AFP0Pt/VLai/C9ozqULoQjHKQvDaypnGHGb2nDvOXGhB93THLSQikQuSqtdccw0Aw4cPB4z0HTRoEFA9T0Dbcb1xZXn2NZ4odvhZyWyxJARfNnNpaWklmNVHNoVWoUaNGjmr1oUXXgiYPcjPP/88YFY5SWT9Xqy9yUHZW37Q/mzt8dXcVaLWz64wr0T2/l5RUVHtgwYNGlS7h2FIaPcxva1X8qG2+P+GDRsiuYfyZo8cOdKxmU855RTAtBYK6Xpam9liqU/4ksxa1SWJJUm073jNmjVOBoxiv7J740oxJLMkijz4ulZt27YF8mu+nklCeyVzFHMsKytzMqKCKLHrRzLLW19Xd9SlI1fJ7MsB5t34rhdVPzds2FCnL2JUWyt1fCXFHHDAAYCpH/bss8/mfcxMv0dJ06ZNnU37QZQL8jOXuD5/UTxbVs22WBKCLzU76SpMVGq22G677QCzLVQbSILsx+RV0aKeo5eQyvoU7R7mSiHztg4wi6WekZcDLI4lbvOlmKu6u8405B6SyiW9NJ1z6LfPYym5CsF9D5OuPWbDSmaLJSH48mYraV4SxJuqlm0LYm3JDen+1vsdr0aQawpjOtIVidf8ZLP6KcqWj02k+SicpzF5j+Edq36vqKjIeF0zFcFXworXLnffpxw2b6T9PJdrkOs9c1+LTMdN159ZWo53fu5jeMeg4+inN6HG+7NBgwY1tt3qunpLV3mf/2xarXeefhsZWMlssSQEXzazxWKJL1YyWywJwb7MFktCsC+zxZIQ7MtssSQE+zJbLAnBvswWS0KwL7PFkhDsy2yxJAT7MlssCcG+zBZLQvh/jfL9gEckUwsAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd72275f8>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1000, D: 1.25, G:0.8063\n"
     ]
    },
    {
     "data": {
      "image/png": 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B0CRIZAALHfKFEBol4gwCcQap9vb2vNC/Us39hRz3+i40ZujfRYsWxQYcFDIAlaqWVCOcLwnijFVh0Ei/fv2yn32f8/tSgirCOdWr82G4ph0dHW5g/fv3z0K+sTBqZIwL8FAIqoxb4XMYPUdPBsQkASjhuxD2r6pJbHahBzapf7NWOntcelmxgn7FrLf19CcmQZqx2fVGoQ1LG3IpvuO40raNtqYWAWYw9DGUFQEWJ3a1tLTE/i3OH5v2Lhj6XUu5bk+qQhShuNVou3ozo9oRcb197YyZDYYmQUWx2YUig0LlPTw27nMp0DlV6EDNr7/1rW8BcMsttwC+SdfixYv5whe+AMBbb73lviv1+qGUkc1me42e1Ywo5V7H/S1qXGrWtTJmNhiaBFW1Zvfw25x/i8WmqgicyvCq3cwaa6wBeJdECBXL1zlnzJjhSvSItRWzG9Gd86zZcZbvQvPVterlpukJfc2aXa9KI2miVGt21VMgS/UNh747dT44/PDDXQ8jvdQ9GZn0EqsmsTrvTZgwgRkzZuQcU4rhK4l/uxJ1QX24lBwgqEPmokWLAK8a9EY0ykuU5PpKcFA/tC222AIgpxTSKaecAviKpXPmzAHqm+prYrbB0CSoupiddAcWM3/9618HYNKkSa4KpIxWN998MwDXXnst4DsriLFKEXGLSAg9RoBFP1fqWmtpaXHdH3feeeeCY5QqoM6Ixx57LJDfOaIU1EvM3mSTTQDfU0prtv/++wO+jnZcl84kKFb6qRTJQM+gqoxOmzYNgHXWWQfIL3m1YMECZ4R97733gO566OCrzz7++OOAjyarBBY0YjD0MSRi5jBUrhrMLGPW9OnTge6uiVOnTgVwfZrT7H5QjJmFYiGApc5Xu/rkyZPZZ599cn4r3TgSawz4Xlp/+ctfAN/PWbaEQYMGuc6E0tnEeIUMfJ/NJTVm1hyHDh3qulyox5SkpxdffBHwHU3Ut6oSlFuUUeuq5+yqq67KGes3v/lNAJ544gnA68O77747J554IuDdpNKnZftRr3BJnLLnlANjZoOhj6HuzBz2LVq0aJHTXdIuTwSl6cyFgkaSznPYsGFAt6VaOpiYYPLkyQAMHjwY8I0C9Bvt/mLk6H2XzvnTn/4UgPHjx+eML01mVo/h4447DoB1110XgGeffZZDDz0UgC9/+cuAZyzZP/T3ali5y2Vm2WYkzYT9ooola0iKkuV7t912A2Ds2LEArLfeeoBfW7F8OTBmNhj6GBL5meP0xXLKuoa+Vu2K119/fU0YudiY4uZZDotoB//tb38LdO/k8+bNA3z/KXUM3HDDDQHPeGKOMMBG4+zo6HB9f6+++urEYysXSy+9NOAttpIcJBVce+21joH1nXo5q29xI+Dkk08G/P1WUFJP65zNZp3+rH///Oc/A97Doh7io0ePBvw9S7MntzGzwdAkqGoEWDnMddhhhwGebc4888xqDikR0gjJlMQh20Amk3HWeu3SshFcd911gG/Epl3+scceA+DII48E4JVXXgG6WaEekVXbbbcd4PV16b9i6s7OTqffi5G0vpJQ0q64EiJ6Pa3JMccck/NZcQ3lQGuoNVJ/bvVnVp9uWe/TSAE2ZjYYmgRVYeZKdpcwimuXXXZx+l9viueNg5hJfsiFCxdywgkn5Jz/pJNOAnxKp3yU2267LdAdcQSNk8whtlGvZcWSR+OSNZetttoK8GOfMmVKzcYJhe0g+v9ll10GwM9+9jPAs6b0+rlz58aeV2w+YcIEAL7zne8A3rqtGIHDDz8c8NF7hVJoq/WcGzMbDE2CmjWOC6HdSM24FeV14oknOj1akU+/+c1vcn7TmxDqiNttt51jAOnGw4cPB2DEiBGAj/NtVChTSBJEoUqY559/PuDjmjUnWe7TQikRevpOPmHZKmbPng344hbCrFmzAJyt48QTT3SehhCK3tO5ZO+ohVRlzGwwNAlq1mw9Doq2ufLKK4FuC+nIkSMBWHnllQF4/vnnAW81/fe//12165cbPVQqFF0kf/OQIUOcXiW9S1btNHbvNCLAxLbKw/7oo48Ab90eOnSoWzPN9fjjjwe8jllNVGsNlaP8q1/9qpwxAH4tVcrq9ttvByqLZiw1AqzuL7Ogl7q1tdUFIShIXYEHCniXa6QaSOtl1ouqJAoVTBgxYoRb2Gqkx/WENF5mGZVWX331nO+1GZ199tkccsghuj7gA2BkPKsmqr2GYaimKt1ss802ACyzzDLO1RTXsVPhyap086c//Qkor3iBhXMaDH0MdTOAhRCDDRgwwBnD7r33XsCb+RXuuNJKKwE+MbwREbbdkcqw7LLLNvS4k2DJJZcEvFFrrbXWAmDttdd27P3hhx8C1SlCUCuIXfXvNddcA3hmfuONN5xa+OCDDwKw11575RwzaNAgAC6//HLAq4gy7qbhbjRmNhiaBA2jM0ebZ2lHlB4i986YMWMAX05nv/32q/i6aRvAVFBg++23B7oDFC688EJdu9qXy0OaOrMSFLROClmdOHGiK0ogN5b+lgbSWkPpzJIaJWW9/vrrrLnmmkA+s+reaL5KwFhhhRUAXJLNt7/9baC7RFEJiR2mMxsMfQkNozMXaikqhj799NMBGDVqFACbbbYZ4BmhnuVNe4JCNyVdjB071rk+FDCjXb6R5xGFmES6shhMUBcR8OWgehNUSEGJF7JYK/R4//3377Gl6yOPPAL4Mr1KQpFVX5LaPffcU7V1N2Y2GJoEqTNzqUXQoztd2Hxavkk55FWSRTpbmgnfpSKumfvbb78N+LIx99xzD+uvvz7g/bTz588HPKP1JssveGZWXEBra6tjm4svvrhu40oKrZlYU7qyfMa77rorkKwI4euvv55zDp1b5zBrtsFgyEPqzBwysvTcZZZZBvB6oti3s7Mzr3+TdGel1ekcyy67LNAYzKxdXUkTKsanFifyLY8cOdJZOh944AHA62hKxVPJmUZJeewJWp+NN94Y6L4XSqyQnzkNlFOuqhgkYSjCUBLTjjvuCJTXJkhjVEF9vQ+KoTBmNhgMeai5NVssJB+xCr1NnDgR6E4ZU0F3MbB2RiX4v//++0B+qlo9IQlDfkTpW4X6VKuZnaKBFGGklMihQ4cCjZ8KKYiZFVPf2dnJM888A/j7kAaq7afXPFQ6WDYb+YjLSfCRXUQlh5U2OnPmzMoGWwDGzAZDk6DmzCwrnvxwZ5xxBuDZd/Lkyfzud78DutuAgLcEa+dXBFWabWvKhXREsaxYV2P94Q9/6AqmX3rppYBnGKUU1tLfHNfGNvpdqeeQ5JTJZHplO1oxs+5/WNo4CcTmDz/8MODb00gfTyNjzpjZYGgSJGLmajTO1m+VvP7UU08BnpmPOOIIVxxNO6SuK+tvkgT3Wjf7lmVdfue9994b8K1lDjnkECdhaHf+17/+BfhoMWWJ1QIhI5dTYE6/VRZVe3u7W9feWOpJkqDaB8nyvOeeewLdkWFicbG3pJLTTjsNwDWW0zOs9r3KlkoDxswGQ5OgosZxFV04yLxRs7SDDjqI1VZbDfBRM7feeivgM1B6qlYR1XH0/yKxtKlk3CiaS03GVUklCvmVL7jgAiCdyK9Ss6YK6cqlSjWy+irHd99993Xlcg466KCyxp0E1V5DzXuPPfYA4MYbbwS8HzoqvSg+XZ4XSWDROG7w5YPKQSplg6r5MhdDKV0JSv1d+EAWqN6Yysus66oGs0ToZ555xr3gSmxPE+W8zJHfJrqWjH6TJk1yCRbqp5UGIht1qmms2qxUPuiEE05walSoCqoa5/e+9z0Ann76aaBi1dRSIA2GvoSymFnoDcaNQkEbwedUd/V6I83+zCF0r9dYYw0nfirNM83rpc3M9YYxs8HQx9CQOnMtYcycLqRbSu+sZrCEMXMujJkNhiZBoqCRsLRPIYbWMWGSfqi76jghdMIXOn+clTvOhZLNZmP/JsYodp5w7MXS1eLmq3PJZdHV1ZXX3aCSbgc9jSeE5h1a99OStsKytUlRLJQyfIbAz6+U64VFMPRvGE5bzr3p6Xko5ZyF5lf0+ERHGwyGhkUindlgMDQujJkNhiaBvcwGQ5MgkQFswIABWfAGgtCYEo1ZjTN8yTURfl/IuBRnPBDirhU1Mug7xc7qOjIutLe3u4G0tbVlo8cUM1aUOrZSEBpiNFZ9r3tW6Bph90GNXd93dHTk3Ohmci8Wck2VMr9KjFL6XWicCs8RGli1PoWMunHXLTS/ouNKsqj9+vXLedgLWah1vkYvRtfIPspokj/4oP0k9zTuQWiUOVYTfS1WIA4mZhsMTYJEYnZPYm4jM3MoFhXy4dW6kEEIiWaDBw8GfKrn3Llzc8YVHV9c7Hm1y9AaGh/GzAZDk6AiZg6/7+rqagjDihiuq6vLMVQYGdQI4wwRtrhRgQOV7w0Nc4WKMBj6LoyZDYYmQVVL7VY7xrgnHTZ0McgKvPTSSwPdLjRZglWSR261QudMk62jdoXQniBGVgmljz76CPDleUPXVOjKgnwbRSNKHr0dUelHrVn1zI0dOxaAMWPGAPmtlGqBiq5UrOayJqmqhKpzpdIyerlUV0kPZjabdQ+v/hYafrRp/OMf/wDg2GOPBXxtYl27tbXVdSGI89XWCnpRR40a5WpKL7/88gCsuOKKgL8Hqp+tmmchoptYXNJL0iD9tKGKpBpvGnWjqw1tsnohVZtuzJgxrgzU1KlTAd8hUh0shLXXXhuAF198MfXxNtaKGwyGslEVGSDK0NrFDjjgAADOPvtsAAYNGpTzG7FuKPYuXrzY1Vy+7rrrAL/rqf+vmFnF09RzSudS14xPP/207gYv7e7qznH++ee77pWhVKJxq6ez6oSr1nIpwSOFRPB6QEysuajDg8Yut5ukk0Zk6tGjRwOwzz77AL7L5Zprruk6l5x55pkA3HDDDQDcdtttgDdeSiKrBYyZDYYmQUXMXIjtQn1WfaHE0OresMkmmwD5vYgKxaqqX5N2OXWNaKTAFDGhmHiNNdYAfG8hzX/gwIFOepEkoc9iM+n8MoB98YtfBOCdd97JuWY2m401fNVDEon2qH788ccBX5QhPEZ1xI855hgAxo8fX6thlgx1alSf5r/+9a8APPfcc04H1v3Xc6wChrJ7HHLIIQDMmjUr9fEaMxsMTYJEiRZJgti1A8tNpKLgsmoXc2OJ5dTLWcwlPaWaqDRIX/NcZpllAK8bn3XWWQCsssoqOcfNmjXL9dnSTn/kkUcCvodvqF+Koc855xzA62cffPBBbMdIsfuiRYtST7SQ7i+WPe+885yUIcu95vjss88CuCL5Yj1ZisuRthoh0WKzzTYDfGOD0HZ08803l31uS7QwGPoYUmNmQSwrpuipT9Qqq6zi9E6FMfb0m0pQ6a4uPX7cuHGA7y2koIL3338fgF/+8pcAXH311Xl9gGXRVSdM+c0333xzjQvwluHLL78c6G6XEuc/l1TzySefpMbMYh+1bfnud7/rri0Pg/yuGqd0aLVtkTRy0kknAXDJJZckHkc9mVlSiaQt2Uq0thtttBHgJZJyYMxsMPQxpB5rJh1IEV89lb6dP3++28XTKEFbLWgeigT69re/nfO9rPb6/oknngAKW5kVuaZug/fffz8AL730EuAtv2LC119/Heje/eMkqzTvnaStfffdF4Dvf//7gF/D6dOnOx9t6D+WxKDIPFl9t956awAuvfRSoPHDUTXXAw88EPAShiCr9nvvvVezMRkzGwxNgtSZWUx13HHHAfCtb30L8J3lZ8yYAfjWp4sWLXJ61csvv5z6uMrFiBEjADj44IMBrzu99dZbgGetJDG5YiP50aVvS/+WlVu+68mTJ8eeK87KXQ2sv/76AFxxxRWA14PlB99mm23yrNJi5O233x7ARcFpboqu0n1Mo1d1NSHru2wlYQH93/3ud4CP6qsFjJkNhiZB6swstlFanxjtvvvuy/leO/GAAQOcjqim5Oussw5QXT2wlOqNhY6R3nrGGWcA3XG64MevyLc333yz7LGttNJKgI/vFbTLX3bZZUBxn2waOqcy3u6++27AW/Llddhmm23cuMRUijOQZX7UqFEArLvuuoBnddkUGjFGO0RbWxv33HMP4DPexMiSVmThr2WUojH3xtZuAAAU60lEQVSzwdAkqFnm9LRp0wAfwyrmEPtGS+Vot5aFUJZB6deyEEunLIYkRfpKaT8q3U/WWvnPJWGcdtppOfMsB+uttx4ASy65JOB3d7FXKQ3Mq8nMWg9JBLJAK5tL+q7sBQMHDnTRbZJcHnnkEQA23XRTwN83SVtHHHFE1cddbUgqGzdunPMn6/nSs3nKKacA9fHEVNQFMgkU8KAEC4lmqjwZLY4vI4gCMBRIoKCKO+64A/DpZ/pcCOUUoy9mPJK4KMONoMWUy6Wch1L3V5tVpFA/4FNC9TKn/eDrfgwbNgyAbbfdFoCPP/4YgKOOOgrwG5fW9rbbbnOqQvjCh6qD5pRGqG61cdNNNwHdIbu6NwpXVZqkjJf1gInZBkOToHYFij5DT2Ft2Ww2j4kUTCExT0HtEyZMALzL5tprr3XnKAeliORyRYXHyiBSicFDBiUZkiSqvfLKK4BPzKiVKKrryGgpiUn/7rnnnoCXnKLljyRpvfDCC0C+60lsfvzxx6c7iSpArkAl0bS1tbl1/tOf/gT4wJ9qIqn71JjZYGgSJGLmehUD0C6vNDqllSnNUEaHJ598EvCBGu3t7YnGXMqxYk1BUsSUKVOAyrpiyN2lgAolmChkVHaGWhuJ3njjDcAbHHWfttpqK8DrwRrXm2++6YoTSGeWNKVjrrrqKqC2QRVJIVecCitKqujq6nLpnhMnTgTSMXglLf1kzGwwNAlqrjNXAjGCkvNl3Vahv0MPPRTwie933XWXS+yvBjKZjLPKqy6ypIawLGuScES5a5T6qB1Z7hz9W68QRwXCyGW2wQYbAJ6xZd0Wy7a2trqiDErXFCOL3U899dRaDL0syHbxhz/8AfCFJ/T8jRs3zjFyGmtSblFGY2aDoUnQq5g5hHQZMbKYTcEm0mNLRU86c1tbm/Mny8Irn7SCIKRXxZ072o9LDHDnnXcCPjhDfuTDDjsMqL9eqbFLZ1eKZhyiVlgFjQiyC0iXbiSICXfYYQcANtxww5y/z549G+i20aRhPwoZOek1jJkNhiZB6swclpWtJuTDDHtNSS+r1OorPVj/ZrNZp0fJ0qmIMIVxPvfccwCukL/mrUi3J5980pXOVcKCwiNV3E5WYv2mtyGTyTjJRTEAsl1MmjSpbuPqCWrUoHgFeRX0HF100UXus54JpXRq/bXukkhkxZcUpvugSLF33nnHpbLeeuutAMyZMwdIbiE3ZjYYmgSpF/RbeeWVAb9z/e1vfwN8G49KMHjwYMBbVYW99toL8E29CkF6SWdnZybyXRb8ThxtZidIr9WOe/HFFwN+55V+q51bCRgqH3PLLbe4gvjS7cXeYjPpZtVAWAwuzYJ3ul+rrrqqs4CL7ZSsryIV1USlBf1UFvjRRx8FfMqt7B+KvxbbDh48mIcffjjnmEqgog5qw6T113Px6aefWkE/g6EvIXWdWfK/4nhVDF99bGUZTeKvk26s7CJFSkkfeeaZZ9yxcfGtpfRnDq2JmUzG7ZbaxRWNpnK5iobSLrvFFlsAvijf2LFjnb4tKBpKRQB7K3QPfv/73zupSWujUrqNBEkSsoOERTAUUXjeeecB3oYxderUkhlZz5T04euvvx6A3/zmN0B36yHlICjjrlgP8aLzSXS0wWBoWCTSmUOdMgnEYIoIErsqoV165Jw5c5y+pesoj1b6liyIYk6Vnt1vv/0Ab1EuZA0MLdSLFy8uWd/KZDJOZ47bPbVji5mkZx199NFAd/NtlcxR5JSKw6XdfuezOVRdZ9a9lDRy/fXXuyIOipRSXH0aKFdnFjMr80kF61U+VzEFhaBiG4pClASo5urRcsiVworgGwx9DDVjZu1c0p21YyvutRDC0jtiRcVDP/TQQwDstttuOcdH9eRwrPpbJK82ETPr9z1F54TRPJImzj77bBfjrN1dUksaUUW1YGZVXbn33nuB7qJ98jAMHz4cSLdQX7nMrGdgyy23BLw0JR22UVAqM9fsZc678GcP+1prrQV4Y0O007zEZF1P/XxUTijOYFTsZRYkGkbN/mm6bSRu33HHHS6w4OSTTwYqq+TZE9J8mXUPJUKrFzfATjvtBPgkjTTRCF0g04SJ2QZDH0PdEi3EmCokoEqU1UAkICTvb6WKydWGQjVnzpzpyh8pWKC3QvdSlSoVXDFnzpxEnTwMhWFlgwyGPoq66cxpIppCFu5u4edoOGctQh2XWmopF/JZi9rKaerMYYKLCvrNmzevpmmbzaozFwo5Lnp8qqMxGAw1QyJm7t+/fxbiGSXKenGMWG6oWqFzhf9qJ4t25FMKpnRkXVduro8//tgNtLW1NWd+4RyqMeaWlpa8sfQ0r7jrFhuPLM0dHR2Z4Pss1K84Y4gk9zi8HxH7hzuJntHwHof/FoKeGz0b4W+i59Sxcm/pmdHzrWP19/D7QvMS9Mzq+6j7tBiMmQ2GJkEiZjYYDI0LY2aDoUlgL7PB0CRIFDRSidm/kk4P4TmEuHMluVbUrSHjiSBjRbHzxRllwnOUOJYejwmvLUOMritDTMQImDOg3uJeLIQ4Q1F7e3ueEVP3Pbw/UXdlkrWpFKFRM4qw5nqYaRWdX9FrpF02qNERfZll6S12T8KXNu4lrvXLUoql97Pvm3oNw/npPuiF6erqahhLvqCxCeH4wjWMg4nZBkOToFcXwU8bhUSiUkX4ejF1yES1uFYjiezhfVd0WrQJQSMgadx1KTBmNhiaBMbMEcRFCxUyXoRs1JMxLlq2t1pMlslkYseYBlumwSZpIYzaSqMJQyWIrk8oLYSlnkuFMbPB0CQwZk6AcvQtxfmqosqrr77qys9WimiMsBC606qBJOWK641QMikUE11vz0OIal2/5i+zHj4ZJtR3STWi5s+f7yo7qtTOj3/8YwC++tWvAj54/Y477gB8Le5qGzcqeSFkgFK9L/Uvmj9/vhvvK6+8AnjfcDh+nSOs0awa49GHIE41qAbq/bAnQdxYo2qO/j9q1CjA1/TeeOONAbj99tsBXzFWvctqDaubbTD0UdScmcXE6jmlmtFipcWLF7vOgT0ZAI444gjA9zdW14ByUapRqxDCsUq83m677QAvZnd0dLgCeBdccAHgmVkdOQT9Rt0IX331VcDXZI6OMS6dstGgjh+bbLIJ4Dt9qEOIujpUq5pnoSgvfff2228DXhRX+SPVOP/BD34AeAlKkuHChQtrIq0YMxsMfRQ1D+fUrqdeO9ILowUBosUFwBe+k+4ybNgwwMeyqiuGOkUk2dUrLTkT1uHWZ3XXUB8p/b29vd313/rPf/4D+E6ZYmaVHZakoTWaPn064EvzFls73ZuwOEEaZYOKjUMSirph6nMIrZn6hEl/LaVYQTTcMUnsuZ4zdd9Up0p151xppZVyriNbxf3335/H2mkytZXaNRj6GGrGzNrdVLhelmrpLyrT2r9/f/eddsrTTz8d8B0FtZPefffdACy33HIArL766gC8++67JY+rXGbWfMSA0v232morAFdOV1Z74d1333X9gMVSKjMcllb64IMPAPjvf/8LwJlnngnADTfcABQvCKhzhyVnqsHMYSE/MVZ0POpk8fTTTwOeBbXOsnPIoqyuGE888QQAX//613scR6FGBtXICtNYJTGpS+QOO+zg/q6GDAcddBDgGzSkAWNmg6GPoWbWbO128+bNAzwji0XV2mTEiBE88sgjANx4440AeWVb1VleeqjOJQYrF+WEQkoamDBhAgBf+9rXgHxGXrBgAdCtY0kXk3V68803Bzw7ie1l+VcJW0kq66+/PtDdN1jF9WsJrWUoUSy99NIAnHXWWa53tnRlteS5+OKLAfjrX/8KwB577AHAMcccA/i1LRfV0F31PMlWs++++wK+t/JOO+3k1vCoo44C4Ic//GHVrl8ujJkNhiZBzZhZ+pT62f7iF78AfE9i7XrLLruss/bG9SuW31XMICtw2DUyKZLsqpIKfvaznwG+h3RYplX6rxqoLVy40Ekjp5xyCuC7WEonFsOFyRPyyapP9V133eU6YYZhnNLL04Q6eIpdxboDBw50vafPOussAJZffnkAXnvtNcBLG4qyUsNAWf9LkZJq0UQAvE3gnHPOAWDVVVdlnXXWAeArX/lKTcZQCoyZDYYmQc0jwKRD/ehHPwJyW8lAt++1p/ha6dfavV966SWgdjs1eCvs7rvvDngmDPX38ePHA/DYY48BcPDBBzuLruwH11xzDeD95WK6vfbaC/CMHFrQt9hiC3deMaHuXZr9kMWq8jLsvffeAC6m/qmnnnJ/+8Y3vgH46CnZARRtJd1Tlnvp0o0Eran8/7fffrvzqFSz4WGlMGY2GJoEdU+BjEvMjkKMLAbQv2L5E044Ic0h5kCseNhhhwFebxQkHSjuV3r80KFDge6WrvITh5bo2267DYCpU6cCcP755wNw3XXXAT5WW/dj4cKFsTHYSRPbS4GY91e/+hUAu+66qxsHwAMPPAB020N+8pOfAD42/dlnnwX8/dpoo40AmDVrFuAZXAxdCmpRGgn8Mym9fuutt3bX1jo3AoyZDYYmQd2ZOUQmk3FsowwbWUT3339/wO+U8kPPnDkz5/tKrh1FofOJYb/73e8CnqkFMbF0ZlmbtYO/++67sVb30AL+8ssvA93+ZPC+bOnQw4YNc2wpdgzrZ1cTgwcPBuBLX/oS4LO+rrzySsDPeamllnJjlw9djCxdUzq9JJwkjCzUyqcrKUcei2HDhrnvNtxwQ8Dr/vLE1AMN9zIPHDjQGZdkJBszZgzgb6pejFNPPRWonrGnlIdDLrQwKEQv4AsvvAB4UfTxxx8H/EuepICCRLkNNtgA8GKu3GIDBgxwbpMQabimZKBTgIde2AJ1np2LSamOW2+9dc4xM2bMAOCNN94oezy1qrSptVYCxty5c10IsV5iGcdk6KuH+G1itsHQJGgYZhbrXHPNNS6wRGZ/MZTYTcEVMoDVCplMxgULCGJzuccUWKB0Rbmhkl4HvAiv8E0xhK752muv5RU00N/iGLsS6P5rrsUqkkoklZgtaUJrphTRWroTy4VYWCm4DzzwgAtcUiENqT4KuT344IOB2j6jxswGQ5OgbsyssEcVKVBgfltbW14on9jtiiuuAGDatGnuWKhdA7B+/frl6coK1jjjjDMA+Pvf/w74xIok0Lx1b2RoUsigbAa65v333x/LwGkws9DTfd5888255JJLAK+7i4F32mknoL6GolIRSkha+9mzZ7vUTq2NDHwjR44EfJFJBTjVQr83ZjYYmgQ1Z2aV9lEghJLYCwUAaDc/7bTTALj00ksBbzkOExHi2qmWip6C+1tbW2OlgDAsNfxcDDpWjCwX0Lnnngv48EndDyX2P/zww7FjrUdPJen2U6ZMyUs4GTt2LOCt2L0BGrss7gr2GT58uHuO9SzKvqN7IJfbvffeC/jgmDRhzGwwNAlSZ+aw2bWse9///veB7nQy8Bbqdddd17GKCtqpBE/oT44rL1uu7tzT71pbW52+qjRFWeF32WUXwO/AKnFUaEyhJCGdbMiQIYAPktl+++1zjlMZWjUFaJSOhroXYt0llljCpa8qzfPyyy8HeldBfUFjVmDOU0895YpKhqWrjj32WMCHfgq16JhpzGwwNAmqzsxi4s022wzwpVO1q4Vlc1U2VoX+hgwZ4nZ4lWIptYNf2rt+V1cXc+fOBXyBBO24Kuw2evRowCfdy+qpsrqtra3ut4oeki9WkW5iaFnrZfkdN25czud6Q9bd++67D/CW64ULF7rChip01yhSRDXQ1dXlnlc9z2pDpCg52YAqLWWVBMbMBkOToKrMPGDAAFeWVDGqe+65JwAvvvgi4JlMrHTggQcCPqli9uzZTkeW3ikmU8pgvaKGFi1axPHHHw/4NMVonDR4S7T852IkSSbt7e1Oz9buHZb6kSQiKUAF19XQrJT516I9jXR7+VblWx89ejTPPfcckK60VK8WPEsuuaSTQCVF6R5oDcXQtZRIjJkNhiZBImYOLXLahfR50003dW1F5HdT+p58xGIjWa+VcC+dcvr06Wy55ZYArL322oCPplG8s3Tp9957L+f6lSKcX+grzmazbgxqJ6tifLJIh+1qhKjfNSwcIKaVtV67unyzkgIaRe9U+qeKQmhNJ06cCNTGpwr1s4x3dHS4woR6RvfZZx/Ar7+y5aQz12KsxswGQ5OgLJ1ZrLPKKqsA3nc8c+ZMly0j3VFtRpQJpd+q9KxyYvX5oYcecrud9BH5MRUJFdd8rFoopovJzyxdf4sttgDgt7/9LeCLF4ipo02+Q4hpJWEoWkhJ/yq1Uw4jV1Of1Lk23XRTwM9VjKy1++lPf1q1azYyFi9e7Ow5yo5SkQKVUdZa1rJJgTGzwdAkSNQ4rl+/flnwrKNcTvmMoxFaioiRvqtd/S9/+QsA1157LVCaZVbRNMrdjWswXg6iTbnCpmNJonbEUiolq3mrYH20nI/8xH/+858BuOiiiwCvX1VDNy7U7vSz77OFjis2R0Xpybeq9VCUl9a6XjpspW15S0U0ck8VVP75z38CXgK76667AN/SRgxeCUptHJfoZW5tbc0GnwGfbhc17kj8VArYpEmTAFxqXKOE9aX1IISdBPUCzJs3z7lw0jRolfoyh8dH12XQoEGAFxml/mi9Vfs6SdfNUpFkI63Vyxy5BlOmTAG8AVSkJIKL68ZSDqwLpMHQx1CWmB2m+UWhnkIqtieRUjWgGw213tVrjXBXj5uj3Iz9+vVzBkat4ZFHHgl4Y46MnArFrQYqSUQopiqlBZWPklH20UcfBSrvd1YIxswGQx9DItdUnI4nhm5paXGFzVTTWgEQhtqiVNeUjpMBr62tLS+sVC4yBfoouaCaaBQbSqmQMVD/poGk7kVjZoOhSZBIZ25ra8vRmWXNVjGz1tZWpzPI4qnP5RSqjysHFNd5Iol1uJC1V/Mr5i4rx23V0xh6ulax34VBKfo34mkoqjOLkRXk09bW5gJjFACjwB7pzNVoOqA5xI2/lN8KnZ2d7ovQ41Lo2QifKx2je6H1jysNFZ6vHIRFO6LXC+9NuIax56xoRAaDoWGQiJkNBkPjwpjZYGgS2MtsMDQJ7GU2GJoE9jIbDE0Ce5kNhiaBvcwGQ5PAXmaDoUlgL7PB0CSwl9lgaBLYy2wwNAn+H0z4VsQmtpAsAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd5f0d198>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1250, D: 1.238, G:0.9796\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd7172cc0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1500, D: 1.1, G:1.124\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd572cbe0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Iter: 1750, D: 1.181, G:1.244\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7f9dd6c9def0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n"
     ]
    }
   ],
   "source": [
    "D_DC = build_dc_classifier().type(dtype) \n",
    "D_DC.apply(initialize_weights)\n",
    "G_DC = build_dc_generator().type(dtype)\n",
    "G_DC.apply(initialize_weights)\n",
    "\n",
    "D_DC_solver = get_optimizer(D_DC)\n",
    "G_DC_solver = get_optimizer(G_DC)\n",
    "\n",
    "run_a_gan(D_DC, G_DC, D_DC_solver, G_DC_solver, discriminator_loss, generator_loss, num_epochs=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## INLINE QUESTION 1\n",
    "\n",
    "We will look at an example to see why alternating minimization of the same objective (like in a GAN) can be tricky business.\n",
    "\n",
    "Consider $f(x,y)=xy$. What does $\\min_x\\max_y f(x,y)$ evaluate to? (Hint: minmax tries to minimize the maximum value achievable.)\n",
    "\n",
    "Now try to evaluate this function numerically for 6 steps, starting at the point $(1,1)$, \n",
    "by using alternating gradient (first updating y, then updating x) with step size $1$. \n",
    "You'll find that writing out the update step in terms of $x_t,y_t,x_{t+1},y_{t+1}$ will be useful.\n",
    "\n",
    "Record the six pairs of explicit values for $(x_t,y_t)$ in the table below."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Your answer:\n",
    " \n",
    " $y_0$ | $y_1$ | $y_2$ | $y_3$ | $y_4$ | $y_5$ | $y_6$ \n",
    " ----- | ----- | ----- | ----- | ----- | ----- | ----- \n",
    "   1   |       |       |       |       |       |       \n",
    " $x_0$ | $x_1$ | $x_2$ | $x_3$ | $x_4$ | $x_5$ | $x_6$ \n",
    "   1   |       |       |       |       |       |       \n",
    "   "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## INLINE QUESTION 2\n",
    "Using this method, will we ever reach the optimal value? Why or why not?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Your answer:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## INLINE QUESTION 3\n",
    "If the generator loss decreases during training while the discriminator loss stays at a constant high value from the start, is this a good sign? Why or why not? A qualitative answer is sufficient"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Your answer:"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.6.5"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 1
}
